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A potentially beneficial new opportunity is emerging around the exchange of energy between electric vehicles and the electrical energy grid, particularly as more low-carbon energy sources are connecting to the grid. Accordingly, this paper presents an optimization framework to activate the potential capabilities of electric vehicles equipped with bidirectional chargers for energy conditioning (including energy management and power quality improvement) of the future distribution networks. The proposed nonlinear optimization seeks to concurrently enhance the operation performance (using the network voltage deviation index) as well as power quality of the grid (using total harmonic distortion index). The proposed model is tested on a 33-bus distribution network to demonstrate its efficiency and performance.

This paper presents sub-transmission and distribution network expansion planning (S&DEP) including distributed generation (DG) and distribution automation (DA) considering reliability indexes. The objective function is to minimize investment, operation, maintenance and reliability costs subjected to AC power flow, system operation and generating units and DG limits, reliability, and distribution automation constraints (including the constraints of protection devices and volt/VAr control mechanism). The proposed model is a mixed integer non-linear programming (MINLP) model which is hard to solve. For this reason, a MINLP problem is transformed to mixed integer linear programming (MILP) model. The validity of the proposed method is investigated in the two synthetic test networks.

In the optimal and economic operation of the power system, generation scheduling is an essential task. Conventional short-term generation scheduling does not regard the huge important operational issues related to the generators, such as initial enterprise costs, maintenance costs, fuel availability, monthly load, etc. Hence, due to the time horizon scheduling of the daily short-term generation scheduling, it is not optimal in the long-term operation while considering the mentioned effects. In this context, this paper proposes a stochastic higher level of scheduling named Stochastic Mid-Term Generation Scheduling of Wind-Thermal systems by considering fixed and variable maintenance costs of generators units. In the proposed model, the 2m + 1 Point Estimate Method is applied to accurately evaluate the uncertainty related to the operation cost wind power and the load uncertainties for the proposed problem. To effectively solve it, a heuristic algorithm named Adaptive Modified Cuckoo Search Algorithm is employed with a novel self-adaptive Wavelet mutation tactic. To assess the performance of the proposed algorithm on solving the problem, the results are compared with the latest algorithms presented in the literature. Numerical results confirm the efficiency and superiority of the 2m + 1 point estimate method model and stability of the novel adaptive modified cuckoo search algorithm on solving the stochastic mid-term generation scheduling of wind-thermal systems problem.

In this paper, a new type of single-level power-conditioning system for photovoltaic arrays connected to the single-phase grid is presented in which transformer-based impedance source inverter (Trans-ZSI) is employed to reduce cost and volume, and meanwhile to improve the efficiency. As a buck–boost inverter, the Trans-Z-source inverter includes just an inverter bridge and a passive network, which itself consists of a pulse transformer and a capacitor. This inverter can be an appropriate alternative for the common structures consisting of inverters and DC/DC converters. In this article, presenting an efficient controlling method, Trans-Z-source inverter is used to connect the photovoltaic system to a single-phase grid. In the controlling structure, the improved INC method is proposed to track the maximum power point, while the proportional–resonance and proportional–integral controllers are employed to inject power to the grid and stabilize the Trans-ZSI capacitor voltage, respectively. Simulations show the functionality of the proposed system.

Traditionally, electric system operators have dispatched generation to minimise total production costs ignoring the flexibility of the AC transmission system (ACTS). One available option to enhance power system security is to harness the flexibility of the ACTS, where a variety of flexible AC transmission system (FACTS) devices can be incorporated in the ACTS. However, utilisation of FACTS devices is limited today due to the complexities that these devices introduce to the AC optimal power flow (ACOPF) problem. The mathematical representation of the full ACOPF problem, with the added modelling of FACTS devices, is a non-linear programming (NLP) optimisation problem, which is computationally burdensome for large-scale systems. This study presents a method to convert this NLP problem into a mixed-integer linear program (MILP) where a certain level of solution accuracy can be achieved for a time budget. In this regard, this study first proposes a linear AC OPF model, using which the OPF solution with the operation of FACTS devices is obtained. In addition, the loadability of the power systems is utilised to quantify the impacts of FACTS devices on improving the security of system. The OPF problem including FACTS devices based on a linearised model is tested on a 6-bus and the IEEE 118-bus test systems.

This paper evaluates the voltage security of the distribution networks in the presence of electric vehicles in the optimization framework. Accordingly, the main objective functions of this optimization problem include maximization of voltage security margin and minimization of operational cost. Also, it is supposed that the electric vehicles are equipped with bidirectional chargers to control active and reactive power in smart distribution networks, simultaneously. The objective functions are subject to the constraints of power flow equations, system operating limits and electric vehicle constraints. The proposed model is implemented on the 33-bus distribution network to evaluate the performance of the proposed optimization scheme for the management of the smart distribution networks in the presence of electric vehicles. The results show that the operational cost and network voltage security margin are reduced in the case of the higher electric vehicle penetration rate when electric vehicles are used for charging active power and reactive power control capability, with respect to the case that does not include electric vehicles.

This paper addresses the stochastic security constrained unit commitment (SSCUC) problem with flexibility resources for managing the uncertainty of wind power generation (WPG). Departing from the traditional flexibility resources such as the thermal units with fast up/down spinning reserves and transmission switching (TS), this paper explores also the use of demand response (DR) and energy storage (ES) systems in an innovative integrated scheme. The proposed scheme utilizes a stochastic optimization framework to coordinate the flexibility resources dealing with the uncertainty of WPGs and equipment failures. The stochastic optimization model is formulated as a mixed-integer linear programming (MIP), and this problem is large and computationally complex even for medium sized systems. Accordingly, we present a novel accelerating decomposition technique aimed at solving this problem and reducing the number of iterations and CPU time. Numerical simulation results on the modified 6-bus system and on large-scale power systems, i.e. IEEE 118 and 300-bus systems, clearly demonstrate the benefits of applying flexibility resources for uncertainty management and the efficacy of the proposed solution strategy for large-scale systems.

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 study presents a novel approach for the contingency constrained phasor measurement units (PMUs) placement. The proposed approach is based on n − k redundancy criterion using robust optimisation. This security criterion ensures observability of the network under any contingency state, containing the loss of up to k PMUs. In the proposed method, the effects of zero injection and power flow measurements as well as possible contingency states (such as branch outage and single or multiple PMU loss) are considered. The non-linear modelling of observability function for measurements is reformulated by linearisation process. The resulting bi-level programming model is solved by its evolution to an equivalent single-level mixed-integer programming problem. The objective function of the optimal PMU placement is aimed at minimising the number of PMUs in a way that guarantees economic goals and the observability of all network buses. The advantage of this model is to significantly reduce the computational burden compared with other methods. The proposed method is tested on modified 7-bus test network, 118- and 2383-bus IEEE test networks. The results of the case studies clearly demonstrate the simplicity and efficiency of the proposed robust optimisation method in different cases.

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.

Integration of wind energy and other renewable energy resources in electrical systems create some challenges due to their uncertain and variable characteristics. In the quest for more flexibility of the electric systems, combination of these endogenous and renewable resources in accordance with strategies of Demand Response (DR) allows an increment/improvement of the demand potential, as well as a more secure, robust, sustainable and economically advantageous operation. This paper proposes a new model for integration of wind power and DR, thus optimizing supply and demand side operations through a price rule Time of Use (TOU), or incentive with Emergency DR Program (EDRP), as well as combining TOU and EDRP together. The problem is modelled using a stochastic Heuristic Multi-Objective Multi-Criteria Decision Making (HMM) method which aims to minimize operation costs and environmental emissions simultaneously, ensuring the security constraints through two-stage stochastic programming, considering various techno-economic indices such as load factor, electricity market prices, Energy Not Supplied (ENS) and Share Weighted Average Lerner Index (SWALI). Comprehensive numerical results indicate that the proposed model is entirely efficient in DR planning and power system operation.

High penetration of renewable energy sources, especially weather-dependent sources, has increased the power systems uncertainties. For any analysis in power systems such as planning and operation, it is essential to confront the stochastic nature of these sources in order to get much more precise results. Since operators need proper strategies and methods to decline negative effects of the stochastic behaviour of renewable power generators, such as total operation cost growth, this paper provides a review of different state-of-the-art approaches from the operator's viewpoint for handling the stochastic behaviour of renewable sources. Hence, in this paper, three different strategies are categorized for stochastic analysis of these sources. The first strategy is mathematical modelling including stochastic dependency and independency, multi-dimensional dependence, forecast and scenarios. Afterwards, demand side management, which is one of the other approaches for dealing with these uncertainties, is investigated and different demand response programs and some methods to model them are presented. Finally, the effect of different electricity market schemes and relevant optimization methods to mitigate the variations of renewable energy sources are discussed. The study demonstrates that an operator should choose one or a combination of these three approaches based on its requirements.

Local Electricity Markets (LEM) and peer-to-peer trading are new mechanisms to encourage the uptake of solar PV and to support the emergence of consumer-centric electricity markets. However, the coordination to trade between consumers and prosumers has different definitions depending on the context and features of the energy system. This paper introduces a new vision: creating virtual LEMs by cooperatively mixing (optimal matching) different load and renewable profiles that complement each other. Since consumer and prosumer profiles change every day (weather conditions or demand behaviors), the dynamic formation of virtual LEMs changes daily. To reward flexibility, Electric Vehicles (EV) are also pooled into forming a virtual LEM. That is, we investigate: What is the value of creating virtual local markets (via clustering)?, and what is the impact of EV flexibility on the creation of virtual LEMs? Through implementing a LEM optimization model with a clustering approach, we analyze the formation of LEMs for a set of end-users in London. Results indicate that a single large LEM (no clustering) is comparatively similar to multiple LEMs (clustering). EV flexibility obtains more revenue in this new marketplace. Findings are encouraging as dynamic virtual LEMs can enable, accelerate and bring scalability for a ubiquitous deployment of LEMs.

The present research aims to formulate competition in a retail energy market in the presence of an Integrated Demand Response (IDR) program to reduce prosumer costs and increase retailer profits. This gives prosumers more degrees of freedom to reduce their energy costs. The retail energy market includes retailers and prosumers equipped with an energy hub containing a boiler for producing heat and combined heat and power (CHP). Retailers aim to maximize profit, whereas prosumers seek to minimize their costs. Hence, a multi-leader-follower game with a bi-level program emerges in which the upper level deals with the profit maximization of each retailer while the lower level considers the cost minimization of each prosumer. The strategic behaviour of each retailer is modelled as a Mathematical Program with Equilibrium Constraints (MPEC) problem. Simultaneously solving all MPECs, which leads to an Equilibrium Problem with Equilibrium Constraints (EPEC), determines the market equilibrium point. The equilibrium point is achieved using mathematical, analytical methods and linearization of nonlinear constraints by accurate techniques. Two different case studies are developed to investigate how the number of retailers influences the market equilibrium point. The first case includes two retailers, while the second case considers an increase in the number of retailers. The results demonstrate that with an increase in retailers' number, their competition increases, causing the prosumers costs to reduce. Furthermore, our results suggest the IDR impact on reduced prosumers cost and increased retailers profit.

In this paper, a novel deterministic approach for the planning of active distribution networks within a distribution market environment considering multi-configuration of wind turbines (WTs) and photovoltaic (PV) cells is proposed. Multi-configuration multi-period market-based optimal power flow is utilized for maximizing social welfare taking into account uncertainties associated with wind speed, solar irradiance and load demand as well as different operational status of WTs and PVs. Multi-period scenarios method is exploited to model the aforementioned uncertainties. The proposed approach assesses the effect of multiple-configuration of PVs and WTs on the amount of wind and solar power that can be produced, the distribution locational marginal prices all over the network and on the social welfare. The application of the proposed approach is examined on a 30-bus radial distribution network.

The main objective of this paper is to determine which voltage regulation strategies best allow grid-tied Photovoltaic (PV) Systems to be integrated into distribution networks. More particularly, this involves performing a literature review on the negative impacts of grid-tied PV systems on distribution networks, and also the current and proposed voltage regulation methods available. The next step is to assess, through simulation, which strategies allow the greatest amount of PV to be integrated with minimal effect on power quality. This will be done by simulating various Medium Voltage (MV) and Low Voltage (LV) radial feeders under varying load conditions and changing solar irradiance levels. Real network data will be incorporated with an accurate model for PV output power to accurately model high penetrations of PV. Our research highlights the necessity of reducing peak loadings through strategies such as energy storage, off-peak loading methods, and reactive power control of inverters to decrease the voltage deviations. It has been shown that voltage rise and reverse power-flows can and should be avoided on distribution feeders. Voltage unbalances can also be easily avoided by following the simple steps outlined in this paper. Now, a proactive approach should be taken by Governments, Distribution Networks System Providers, and customers with PV to ensure that as penetrations increase, Power Quality is not diminished.

Electric spring (ES) as a novel concept in power electronics has been developed for the purpose of dealing with demand-side management. In this paper, to conquer the challenges imposed by intermittent nature of renewable energy sources (RESs) and other uncertainties for constructing a secure modern microgrid (MG), the hybrid distributed operation of ESs and electric vehicles (EVs) parking lot is suggested. The proposed approach is implemented in the context of a hybrid stochastic/robust optimization (HSRO) problem, where the stochastic programming based on unscented transformation (UT) method models the uncertainties associated with load, energy price, RESs, and availability of MG equipment. Also, the bounded uncertainty-based robust optimization (BURO) is employed to model the uncertain parameters of EVs parking lot to achieve the robust potentials of EVs in improving MG indices. In the subsequent stage, the proposed non-linear problem model is converted to linear approximated counterpart to obtain an optimal solution with low calculation time and error. Finally, the proposed power management strategy is analyzed on 32-bus test MG to investigate the hybrid cooperation of ESs and EVs parking lot capabilities in different cases. The numerical results corroborate the efficiency and feasibility of the proposed solution in modifying MG indices.

The ever-increasing penetration of wind energy generation (WEG) and electric vehicle (EV) batteries in power systems could bring two significant challenges to day-ahead energy balancing markets. First challenge, the uncertainty of WEG and random behavior of the EV batteries can raise energy imbalance in energy markets. Second challenge, the intermittent nature of WEG and uncontrolled EV batteries charging, bring high congestion costs and new congestion patterns in transmission system. In this condition, there is no guarantee that the WEG is deliverable throughout the power system. The hourly coordination of WEG units with EV batteries can play important roles in addressing the first challenge. Similarly, to addressing the second challenge, a transmission impedance adjustment (TIA) device is taken as a promising way to reduce transmission congestion and promote the integration of large-scale WEG and EV batteries through controlling the power flows. However, utilization of TIA device is limited nowadays due to the complexity of this device in the optimization problem of day-ahead energy market clearing with the DC approximation of the power flow network. Consequently, a computationally efficient methodology, which is compatible with existing customary solvers, is proposed to adjust the TIA device set point with minimal modification efforts. An adaptive robust optimization formulation is adapted in the proposed problem to handle the WEG uncertainty. Finally, the simulation results on six and IEEE 118 bus test systems suggest that: 1) substantial economic savings can be achieved through utilization of WEG and storage capability of EV batteries, beyond the independent capabilities of TIA technology; 2) the TIA device plays a critical role in removing transmission congestion; and 3) the storage capability of EV batteries could relieve the uncertainty of WEG and increase its dispatchability.

Sectionalizing switches (SSs) have been installed in Distribution networks with the aim of providing maneuver and ring points, so raising service reliability. It is usually assumed that these switches are totally reliable although, in practice, they have not ideal efficiency at all times. In fact, the switches might occasionally face failure, which decreases their capability with the aim of enhancing system reliability. A model with the aim of considering the failure probability of the switches in their optimal placement issue is proposed in this paper. The model aids with the aim of minimizing the whole costs of SSs and the interruption costs incurred through interrupted users. This study uses the discrete Markov chain model with the aim of obtaining the malfunction possibility under various states. Afterwards, the placement problem is solved with the aim of obtaining the global optimal solution with genetic algorithm. The advantage of the suggested model has been investigated according to sensitivity analysis and various scenarios. The research illustrates how the outcomes of the placement issue have been influenced through the uncertainty arising from the possible failure of the switches and how ignoring the uncertainty is able to cause deviation of the anticipated profit from the real one. The efficiency of the suggested procedure has been evaluated and shown through investigating on the Ahwaz city distribution network. The outcomes of simulation prove the ability and precision of the suggested procedure.

Incentive-based regulations, toward higher performance networks, are the main driver for minimising losses in distribution systems. On the other hand, more renewable generation is needed to achieve environmental targets. Hence, a multiobjective model is introduced in this study seeking to minimise energy losses as well as maximise renewable generation in radial distribution systems (RDSs). Two alternative control strategies of future smart grids such as reactive power management using adaptive power factor control and coordinated voltage control are considered in the optimisation problem. The problem is subjected to the various technical constraints such as voltage limits, thermal limits and reactive capability limits of photovoltaic (PV) penetration, power factor regulations and underload tap changer adjustment. Also, the uncertainties of load and renewable generation are considered, too. Then, the obtained non-linear programming problem is relaxed and reformulated as a well-suited and computationally efficient second-order cone programming problems. To obtain more efficient and evenly distributed Pareto set to help decision-making process, a modified normal boundary intersection method is introduced for solution methodology. The implementation of the proposed framework on IEEE 33-bus RDSs shows the gains that the flexibility provided by innovative control strategies can have on energy loss reduction and PV capacity.

This paper proposes a novel method based on information gap decision theory to evaluate a profitable operation strategy for combined heat and power units in a liberalized electricity market. Risk levels can be assessed using this technique, taking into consideration whether the generation company is either risk-taking or risk averse. The test system used in this paper comprises conventional thermal, cogeneration, and heat-only units. The pool price is considered to be uncertain while an information gap decision theory method is employed to model its volatility around the estimated value. Profits lower than the expected value are optimized using the proposed method and the related strategy is determined. The presented method optimizes the opportunities to make use of high profits or high pool prices. To verify the performance of the proposed method, the model has been implemented on a case study.

The following article published in IET Generation, Tranmission and Distribution , Nikoobakht, A., Aghaei, J., Mardenah, M., ‘Optimal transmission switching in the stochastic linearised SCUC for uncertainty management of the wind power generation and equipment failures’, Gener. Transm. Distrib. , 2017, 11 , (10), pp. 2664 – 2676, doi:10.1049/iet-gtd.2016.1956, has been withdrawn by agreement between the authors, the Editors-in-Chief, Innocent Kamwa and Christian Rehtanz, and the Institution of Engineering and Technology. This is because the incorrect text was uploaded due to a technical error. The correct version has now been published as: Nikoobakht, A.,Aghaei, J., Mardenah, M. et al, 'Moving beyond the optimal transmission switching: stochastic linearised SCUC for the integration of wind power generation and equipment failures uncertainties', Gener. Transm. Distrib. , 2018, 12 , (15), pp. 3780 – 3792, 10.1049/iet-gtd.2017.0617.

This study introduces a probabilistic optimisation model for allocation of renewable distributed generations (DGs) in radial distribution networks. The methodology is based on a probabilistic generation – load model that combines all possible operating conditions of the wind-based DG units as well as load levels with their probabilities. A multiobjective performance index is extracted that is formulated as a combination of two indices, namely energy losses reduction and voltage improvement. Besides, a probabilistic AC optimal power flow is used to determine the optimal allocation of wind DG and maximise the multiobjective performance index. Two alternative control approaches of the future smart grids, i.e. area based under load tap changer control and adaptive power factor control, are assessed to maximise potential benefits and expand the penetration level of DGs. At first, this problem is formulated as a mixed-integer non-linear programming (MINLP) which leads to a computationally NP-hard problem. Accordingly, the obtained MINLP problem is relaxed and reformulated in the form of a well-suited second-order cone programming problem which is computationally efficient scheme to be solved. The implementation of the proposed framework on 4-bus and IEEE 33-bus radial distribution systems shows the performance of the proposed optimisation mechanism.

Distributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network. The authors strongly recommend this review to researchers, scientists and engineers who are working in this field of research work.

In this paper, a solution to the optimal power flow (OPF) problem in electrical power networks is presented considering high voltage direct current (HVDC) link. Furthermore, the effect of HVDC link converters on the active and reactive power is evaluated. An objective function is developed for minimizing power loss and improving voltage profile. Gradient-based optimization techniques are not viable due to high number of OPF equations, their complexity and equality and inequality constraints. Hence, an efficient global optimization method is used based on teaching–learning-based optimization (TLBO) algorithm. The performance of the suggested method is evaluated on a 5-bus PJM network and compared with other algorithms such as particle swarm optimization, shuffled frog-leaping algorithm and nonlinear programming. The results are promising and show the effectiveness and robustness of TLBO method.

Plug-in electric vehicles (PEVs) have several capabilities to help the power system; for instance, one of them is participation in the energy market via vehicle to grid (V2G) technology. Due to some uncertain parameters such as battery state of charge (SOC), PEV availability, etc, they have stochastic behavior and it can affect the settlement of energy market. Accordingly, this paper proposes a framework for the stochastic clearing of electricity energy market in the presence of the PEVs while the uncertainties of PEVs and the synchronous generators are considered. The proposed stochastic energy market is cleared in two steps: at the first step, the random scenarios are generated using Monte-Carlo Simulation (MCS); then, at the second step, the stochastic market-clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The network Total Cost (TC) is defined as an objective function and it is minimized for each scenario. In other words, the stochastic energy market is cleared based on the selection of the cheaper and proper energy providers, PEVs and synchronous generators, while the constraints are satisfied. The proposed optimization formulation is in the form of mixed integer nonlinear programming. Consequently, a heuristic based particle swarm optimization method has been adopted for the solution strategy. The effectiveness of the proposed method is examined based on a real case low voltage distribution system with 134 nodes.

This study investigates the application of a robust method to solve the problem of security constrained unit commitment (SCUC) with flexible resources for managing the uncertainty of significant wind power generation (WPG) to sustain the load-generation balance. The flexible resources include up/down ramping capability of thermal units, hourly demand response, energy storage system and transmission switching action through an integrated scheme. The application of mixed-integer linear programming to deal with the SCUC problem with flexibility resources has been discussed in this study using information-gap decision theory (IGDT) to realise a robust strategy for power system decision maker. Besides, this study proposes an effective solution strategy based on Benders' decomposition to solve the proposed problem. Numerical simulation results on the modified six-bus system and IEEE 118-bus system clearly demonstrate the benefits of applying flexibility resources for managing the WPG uncertainty and validate the applicability of the proposed IGDT-based SCUC model.

Stochastic security constrained unit commitment (SSCUC) based on a DC model could be problematic in AC networks because the DC model is potentially inaccurate. However, solving SSCUC problems using an AC model is still very challenging. Accordingly, an improved linearised AC optimal power flow (ILACOPF) model is provided in order to optimise the SSCUC problem. The proposed SSCUC model includes a linear representation of network losses and reactive power and bus voltage magnitudes. Moreover, in this study, transmission switching (TS), a powerful tool for grid side flexibility, is introduced and utilised to facilitate the mitigation of the uncertainty of wind power generation. Nevertheless, solving the SSCUC problem with TS in a full AC network is still one of the challenges in practical implementation, which is facilitated by the proposed ILACOPF model. Additionally, the aim of this study is to develop a more accurate power flow model to obtain a more realistic SSCUC solution using TS. The proposed ILAC-SSCUC model using TS is formulated as a mixed-integer linear programme, being solved by the proposed effective solution approach based on Benders’ decomposition. Numerical simulations on a 6-bus and IEEE 118-bus systems have been performed to evaluate the effectiveness of the method.

Demand response (DR) programs are becoming a critical concept for the efficiency of current electric power industries. Therefore, its various capabilities and barriers have to be investigated. In this paper, an effective decision model is presented for the strategic behavior of energy service providers (ESPs) to demonstrate how to participate in the day-ahead electricity market and how to allocate demand in the smart distribution network. Since market price affects DR and vice versa, a new two-step sequential framework is proposed, in which unit commitment problem (UC) is solved to forecast the expected locational marginal prices (LMPs), and successively DR program is applied to optimize the total cost of providing energy for the distribution network customers. This total cost includes the cost of purchased power from the market and distributed generation (DG) units, incentive cost paid to the customers, and compensation cost of power interruptions. To obtain compensation cost, the reliability evaluation of the distribution network is embedded into the framework using some innovative constraints. Furthermore, to consider the unexpected behaviors of the other market participants, the LMP prices are modeled as the uncertainty parameters using the robust optimization technique, which is more practical compared to the conventional stochastic approach. The simulation results demonstrate the significant benefits of the presented framework for the strategic performance of ESPs.

In this paper, a robust solution algorithm has been presented for SCUC problem including the uncertainties of wind power generation (WPG). Different flexibility resources have been considered in the proposed model, namely, transmission switching (TS) action and energy storage (ES) systems for managing the uncertainty risk of significant penetration of WPGs to sustain the load-generation balance. Indeed, the flexibility resources have been allocated using a robust optimization model based on the information gap decision theory (IGDT). That is, the IGDT reveals the robust optimal decision variables against the wind power uncertainties without requiring the probability density function of WPGs that leads to low computational burden. The optimization SCUC model is formulated as a mixed-integer linear programming (MIP), being solved by proposed effective solution strategy based on Benders decomposition (BD) technique. Numerical simulation results on the modified 6-bus system and IEEE 118-bus system, clearly demonstrate the benefits of applying flexibility resources for managing the high penetration of WPG uncertainty and validate the applicability of the proposed IGDT-based SCUC model.

This paper proposes a stochastic model for the scheduling of short-term AC security-constrained unit commitment (AC-SCUC) considering reliability and the value of lost load (VOLL). The uncertainty of load and wind power generation, active and reactive power losses, voltage profile of the network’ buses and congestion management for different VOLLs are investigated in this paper. Furthermore, the random outages of generating unit and transmission lines are modeled based on the scenario trees in the Monte Carlo simulation and the reserve requirements of the power system are implicitly scheduled based on the VOLL and by considering corrective actions of the generation units. A computationally efficient two-stage algorithm based on bender's decomposition is proposed to solve the proposed problem. The first stage deals with the base case where all the network components, the units' outputs and on/off status can be determined based on the forecasting load and wind farms' output. The second stage investigates the stochastic part of the problem and runs the possible scenarios in parallel for all the network elements and the available units of the base case. In the case of any violation for a scenario, a bender's cut is added to the first stage which modifies the commitment state and the power units' outputs in order to tackle the violation for that scenario. The method is applied to the IEEE 118/300-bus test system to assess its applicability and capability.

The high penetration of renewable energy sources (RESs) has imposed new challenges in the operation of smart distribution networks. Changing the distribution networks indices is one of the untoward significant changes. In this paper, to improve the operational performance of smart distribution network, the application of electric spring (ES) as a new device in the power electronic revolution is proposed. Furthermore, the novel static model of the ES is presented and described in detail. In the next step, the effect of the presence of ES on distribution network indices in various scenarios has been studied. Finally, the suggested solution has been tested on 32-bus IEEE test microgrid as a distribution network by the GAMS software to analyze the potential and effectiveness of the ES in improving the microgrid indices. The results of the simulations verify the suggested solution completely by modifying the mentioned indices, and enhancing the conditions for the operation of the microgrid.

In order to decrease operation costs in a security constrained unit commitment (SCUC) problem, strategies such as transmission switching (TS) can be utilized. However, having no limit for the switching of circuit breakers (CBs) in the system is troublesome, since a high number of switching increases the failure probability and decreases the reliability of a CB, in addition to resulting in shorter CB lifespans and higher maintenance and operating costs. In this paper, the reliability of CBs is integrated into the SCUC problem with TS to limit its switching. Since the higher reliability of CBs will increase the reliability of the system, it can be inferred that the reliability of CBs will affect the amount of load shedding. A linearization method is presented to linearize the CB reliability equation. Also, an improved linear AC optimal power flow (ILACOPF) with dynamic thermal line rating (DTLR) which considers weather conditions is used in the model to further reduce the number of switching. In order to evaluate the suggested approach, numerical testing is performed for 6-bus and large-scale 118-bus IEEE test systems with different scenarios.

The primary energy source of power systems mostly rely on natural gas resources. Thus, the co-expansion planning of electricity and gas networks can results in more accurate and cost-effective solutions for both networks. The current study presents a co-expansion planning model considering energy distributed generation resources for electricity and gas networks. In addition, when demand sides resources are connected to multi-carrier systems (MCSs), the gas and electricity network correlation will increase which in turn, can affect the both systems performance. In this paper, a model in the demand side is presented based on smart energy hub. In this model, a part of demand including electricity, gas and cooling can be fulfilled via energy hubs in the demand side. The rest of the required energy is supplied through upstream network by replacing various energy carriers. The given problem is composed of binary and continuous variables, and also there are non-linear and non-convex constraints of power flow. This issue increases the complexity of the problem. Thus, a MILP model is developed using linear techniques. Despite the heuristic methods, the proposed model does not need iterative approaches. In this method, the convergence of the problem to optimum response is guaranteed. The numerical studies show the capability of the proposed method.

The work reported in this paper jointly addresses two major challenges in modern power systems: 1) systematically maximizing wind power generation (WPG) utilization under worst-case uncertainty and 2) employing mixed integer-nonlinear programming (MINLP) in the co-optimization of variable reactance devices (VRD) and transmission switching (TS) in an AC optimal power flow problem (ACOPF). The first challenge is solved by proposing an interval based robust approach to identify the worst-case WPG uncertainty. Similarly, to overcome the second challenge, a tri-level decomposition algorithm is used to decompose the MINLP representation into one consisting of one mixed-integer linear programming (MILP) and two nonlinear programming (NLPs) problems. Finally, the effectiveness and efficiency of the proposed model is shown by analysing results from testing on the modified RTS-96 system.

Congestion in transmission lines is an important topic in power systems and it continues to be an area of active research. Various approaches have been proposed to mitigate congestion especially immediate ready ones such as Congestion Management (CM) and Transmission Switching (TS). Using either of the two or their combination (CMTS) may have undesirable consequences like increasing operational costs or increasing the number of switching of transmission lines. More switching aggravates system reliability and imposes extra costs on the operator. In this paper, a multi-objective model is introduced which reduces overall operation costs, the number of switching in transmission lines, and the congestion of lines, compared to available approaches which employ congestion management and TS simultaneously. To verify the performance of the proposed model, it is implemented using GAMS and tested on 6- and 118- bus IEEE test systems. A benders’ decomposition approach was employed.

This paper presents an AC optimal power flow (AC-OPF) model including flexible resources (FRs) to handle uncertain wind power generation (WPG). The FRs considered are thermal units with up/down re-dispatching capability, corrective topology control (CTC), and thyristor-controlled series capacitors (TCSC). WPG uncertainty has been modeled through a proposed interval-based robust approach, the goal of which is to maximize the variation range of WPG uncertainty in power systems while maintaining an adequate reliability level at a reasonable cost with the aid of FRs. However, utilization of FRs (especially CTC and TCSC devices) is limited due to the difficulty of their incorporation in the AC-OPF. The optimization framework of the full FR-augmented AC-OPF problem is a mixed-integer nonlinear programming (MINLP) in which the solution for large-scale systems is very hard to obtain. To solve this issue, this paper uses a two-stage decomposition algorithm to decompose the MINLP representation into a mixed-integer linear program (MILP) and a nonlinear program (NLP). Finally, the robust AC-OPF model with FRs is implemented and tested on a 6-bus and the IEEE 118-bus test systems to evaluate its efficiency and performance.

As renewable energy sources (RESs) penetration increases in the power system, the transmission system operators face new challenges to ensure system reliability and flexibility while ensuring high utilisation of uncertain RES generation. Controllable transformers with on-load tap changers and phase shifting capability are the promising flexibility tools to keep the system acceptable security and flexibility levels by controlling the voltage levels and energy flow. The AC optimal power flow (AC OPF) with detailed modelling considerations such as the bus voltage magnitude by including these devices is challenging. This study develops the AC OPF model to propose a robust flexibility optimisation framework for daily scheduling problem with uncertain wind energy sources. Nevertheless, the proposed formulation representation is an intractable mixed integer nonlinear programming (MINLP) while it includes AC grid constraints and the augmented modelling of the mentioned transformers. Accordingly, the proposed MINLP problem has been converted into a mixed-integer linear program where a certain level of solution accuracy can be achieved for the available time budget. The effectiveness of the proposed method is demonstrated using a modified 6-bus and IEEE 118-bus test systems. © 2019. This is the authors' manuscript to the article. The final authenticated version is available online at: http://dx.doi.org/10.1049/iet-rpg.2018.5136

This paper presents the design of a single-phase electric vehicle (EV) on-board bidirectional charger with the capability of power conditioning. This charger can control its charging/discharging active power based on the demand of EV battery/network or load. Also, it controls reactive power and harmonic current based on the characteristics of the nonlinear and linear loads. The topology of the proposed charger consists of the bidirectional ac/dc and buck-boost dc/dc converters, where it can operate in four quadrants in the active-reactive power plane with the capability of harmonic compensation. In the next step, this paper presents a suitable control strategy for the bidirectional charger according to the instantaneous active and reactive power (PQ) theory. Based on the PQ theory, the active and reactive power that includes average and oscillatory components obtained, based on the demand of nonlinear/linear loads and EV battery. Then, the reference current of ac/dc converter of the charger and battery is obtained, and in the next step, the situation of the charger switches is determined using output signals of the proportional-integral and proportional-resonant controllers and pulsewidth modulation. Finally, the proposed approach is validated and implemented in the OPAL-RT to integrate the fidelity of the physical simulation and the flexibility of the numerical simulations. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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.

This paper expresses the planning model of the backup distributed generation (DG) and lines hardening and tie lines in distribution networks according to resilient architecture (RA) strategy under natural disaster conditions such as earthquakes and floods. Indeed, the proposed deterministic problem of resilient distribution system planning considers the minimization of the daily investment, operation and resiliency (repair and load shedding) costs as objective functions subject to constraints of AC power flow equations, system operation limits, planning and operation model of backup DG and hardening and tie lines, as well as network reconfiguration formulation. The problem formulation is based on a mixed integer non-linear programming (MINLP) model, which is converted to a mixed integer linear programming (MILP) model on the basis of Benders decomposition (BD) approach using linearization approaches to achieve the optimal solution with the lower computational efforts and error. Besides, a hybrid stochastic/robust optimization (HSRO) based on the bounded uncertainty-based robust optimization (BURO) and a scenario-based stochastic optimization is used to model the uncertainties of load, energy price and availability of the network equipment under the extreme weather conditions. Finally, the proposed RA strategy is applied on 33-bus and 119-bus distribution test systems to investigate its capabilities in different case studies.

This paper presents an optimal framework for the resilience-oriented design (ROD) in distribution networks to protect these grids against extreme weather events such as earthquakes and floods. This strategy minimizes the summation of daily investment and repair costs of back up distributed generation (DG), hardening and tie lines, operation cost of network and DGs, and load shedding cost. Also, it considers AC power flow equations, system operation limits and planning and reconfiguration constraints. This problem is generally a mixed integer non-linear programming (MINLP) problem, but it is converted to a mixed integer linear programming (MILP) problem to achieve a globally optimal solution with a low computation time. Moreover, the Benders decomposition (BD) approach is used for the proposed problem to obtain higher computation speed in large scale networks. In addition, this problem includes uncertain parameters such as load, energy price, and availability of network equipment in the case of extreme weather conditions. Hence, a scenario-based stochastic programming (SBSP) approach is used to model these uncertain parameters in the proposed ROD method, based on a hybrid approach, including roulette wheel mechanism (RWM) and the simultaneous backward method. The proposed problem is simulated on 33-bus and large-scale 119-bus distribution networks to prove its capabilities in different case studies.

The paper presents a subislanding approach based on two algorithms, a backtracking algorithm, which is used to detect the exact location of distributed generation based on renewable energy sources (DGRESs) to establish subislands in the distribution networks and a protection co-ordination algorithm, which is used to identify the fault location. The IEEE reliability busbar test system is used for algorithm qualification, with several case studies and sensitivity analyses employed to verify the proposed algorithms. The probabilistic-based analytical technique along with the customer interruption cost model is then used to evaluate reliability improvements. The results presented in the paper indicate that the proposed approach is effective in increasing the reliability of power distribution systems with high penetration of DGRESs while providing an online reliability assessment.

The concept of electricity markets in the deregulated environment generally refers to energy market and reactive power market is not paid attention as much as it deserves to. However, reactive power plays an important role in distribution networks to improve network conditions such as voltage profile improvement and loss reduction. Plug-in electric vehicles (PEVs) are mobile sources of active and reactive power, capable of being participated in energy market, and also in reactive power market without battery degradation. Active and reactive powers are coupled through the ac power flow equations and branch loading limits, as well as PEVs and synchronous generators capability curves. This paper presents a coupled energy and reactive power market in the presence of PEVs. The objective function is threefold, namely offers cost (for energy market), total payment function (for reactive power market), and lost opportunity cost, all to be minimized. The effectiveness of the proposed coupled energy and reactive power market is studied based on a 134-node microgrid with and without PEV participation.

Electric vehicles, including both PEVs and PHEVs have been recently interested to a large extent in global markets due to their capabilities. These plug-in vehicles are able to absorb/inject power from/to the electric grid that turns them into an interesting solution for the power systems. However, large numbers of such plug-in vehicles can be a threaten to power systems. In this regard, it seems necessary to investigate the problems caused by the uncertain driving nature of such electric vehicles. On the other hand, the opportunities provided by the presence of a large fleet of plug-in vehicles as mobile storage/load should be discussed. For this end, this paper reviews the challanges and the problems caused by charging/discharging of PHEV/PEVs in large numbers and investigates their capabilities as a solution to integrate the RESs and demand response programs in power systems.

This paper presents an effective optimal design procedure for brushless permanent magnet (BLPM) machines based on an adaptive metaheuristic optimization technique. 2-D analytical expressions for the calculation of the magnetic flux density, electromagnetic torque, back electromotive force, and self-inductance and mutual-inductance are employed to optimally design a slotless brushless motor with surface-mounted magnets. The proposed approach combines the computational accuracy of the 2-D analysis and the computational speed due to the analytical expressions. The objective functions are the power losses and the motor volume to be simultaneously minimized subject to three constraints: 1) the required electromagnetic torque; 2) the required maximum rotational velocity; and 3) the limit of the stator core flux density. The optimization problem is solved using a fuzzy adaptive particle swarm optimization (FAPSO) technique. To evaluate the efficacy and effectiveness of the FAPSO technique, its results are compared with those of the conventional PSO and genetic algorithms. To investigate the influence of the armature current waveforms on the design results, three different optimization problems with different armature current waveforms (ideal rectangular, six-step, and sinusoidal), but identical objectives, constraints, and optimization variables are defined and solved. Finally, a BLPM machine has been designed and manufactured to experimentally show the effectiveness of the proposed technique.

The unpredictable and volatile nature of wind power is the main obstacle of this generation source in short term trading. Owing to the ability of demand side to cover wind power imbalances, aggregated loads have been presented in the literature as a good complementary resource for the wind generation. To this end, this paper proposes a technique to obtain the best offering strategy for a hybrid power plant consisting of a wind power producer and a demand response provider in the power market. In addition, conditional value-at-risk is used to limit the risk on profit variability. Finally, a detailed analysis of a realistic case study based on a wind farm in Spain has illustrated that joint operation of wind power producers and demand response providers can increase the expected profit and reduce the potential risks.

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

To meet the fast growth of electricity demand, the traditional network solution tends to expand existing substations, build more new substations, and build transmission lines. Distributed Generation (DG) is posed as an alternative method for the network providers not only to accommodate the load increase and relieve network overload, but also to offer other additional technical and economic benefits. This paper addresses the issue of DG planning and has proposed a technique for optimizing the DG size and location to minimize the overall investment and operational cost of the system. The proposed optimization methodology assesses the compatibility of different generation schemes in terms of their cost factors that can be significantly contributed by a DG. The direct and indirect costs of power supply quality, reliability, energy loss, total power operation, and DG investment are used as key cost components of the DG siting and sizing strategy. The Particle Swarm Optimization (PSO) method is applied to obtain the optimal DG planning solutions. Finally, the proposed approach is tested on a distribution feeder of an Australian power network. Simulation results are presented to illustrate the feasibility and effectiveness of the proposed method.