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This paper presents a multi-objective optimal operation of meshed AC/DC power grids including multi-terminal voltage-source-converter-based high-voltage direct current (VSC-MTDC) systems. The proposed approach is modeled as a corrective security-constrained optimal power flow (CSC-OPF) problem, with the minimization of both the operation cost and power loss as the objectives. Moreover, it provides a cost-effective solution to assist in decision-making, and improves the system security during operation. The N − 1 contingency security criterion is enforced for both AC and DC transmission networks, and corrective control is used to eliminate or alleviate post-contingency security violations. The corrective control actions used in this paper include not only secure operation control actions, but also economical post-contingency corrective control of the multi-terminal VSC-HVDC. To increase the computation speed, a contingency screening technique is applied to CSC-OPF by efficiently selecting the most severe case of the N − 1 contingency, as obtained using a voltage security index (VSI). The proposed approach uses the non-dominated sorting genetic algorithm (NSGA-II) to find multi-objective OPF solutions by checking the post-contingency state feasibility while taking into account post-contingency corrective actions. Simulation results confirm the validity and effectiveness of this approach.

In this paper, selling price determination problem for an electricity retailer has been studied. In the proposed model, the selling price is determined under fixed pricing (FP), time-of-use pricing (TOU) and real-time pricing (RTP). It is shown that the selling price determination based on RTP by the retailer can lead to the higher expected profit. Furthermore, to exchange power between retailer and power market, the bidding and offering curves should be prepared to bid and offer to the day-ahead market. Therefore, this paper proposes an information gap decision theory (IGDT) for obtaining of optimal bidding and offering strategies of retailer. IGDT assesses the robustness and opportunity decisions of optimal bidding and offering strategies in the presence of market price uncertainty while retailer considers whether robustness decision (risk-averse) or opportunity decision (risk-taking). It is shown that risk-aversion and risk-taker influence the expected profit and optimal bidding and offering curves. Meanwhile, the scenario-based stochastic framework is used for uncertainty modeling of market prices, client group demand and variable climate condition containing temperature, irradiation and wind speed. To validate the proposed model, three case studies are used and the results are compared.

The priority to reactive power contribution from the Voltage Source Converter (VSC) based High Voltage Direct Current (HVDC) connection to support the grid during faults as suggested by the modern Network Code (NC) for HVDC affects the distance protection of transmission lines. Moreover, suppressing the negative sequence current during an unbalanced condition also interferes with the proper operation of the distance relays. This is because the current contribution from the converter is limited in magnitude and modified in the waveform in order to protect the power electronic devices during the fault in comparison to the synchronous generator fault current characteristics. This paper discusses the cause as well as the severity of the problems faced by the distance protection of transmission lines connected to the VSC based HVDC system by analyzing the apparent impedance analytically and in the simulation. The response of the relay to balanced and unbalanced faults lying on transmission lines is investigated. It is shown that the VSC limited reactive support and suppressed negative sequence current affect fault detection, forcing the relay to malfunction. The results of this paper can be used as a reference for understanding the effects of VSC-HVDC system on the operation of the distance protection during faults.

The use of a static var compensator (SVC) as a component of flexible alternating current transmission system (FACTS) devices to control power systems has been investigated for decades. Its aim is to regulate the system voltage and improve the stability and loadability of power systems. A typical assumption in such a system is that the parameters of the controlled system are known accurately, which is rarely satisfied in practice. This paper explores the development of a simple but effective controller for a single-machine infinite-bus power system with SVC subjected to both matched and mismatched disturbances where the controller derivation is based on the assumption that all parameters used in the system modeling are unknown , but bounded in size. The research in this paper illustrates how an indirect robust control can be incorporated with a modified disturbance observer-based feedforward term to attenuate the influence of parameter variations and disturbances from the outputs of the system. Simulation results are presented to confirm the effectiveness of the proposed scheme.

The paper presents an important aspect in the research and development process of control algorithms of FACTS devices. It reveals the details of the time-optimal current control (TOCC) algorithm for STATCOM and deals with its implementation on the digital signal processor (DSP). It has been recognized that discretization of the control process within the DSP deteriorates the operation of the controller and consequently the device itself and calls for new control algorithm improvements, which are addressed within the paper. To evaluate the modified implementation of the control algorithm, the results of the ideal EMT simulations within the PSCAD are compared to the results of the real-time simulations of the controller hardware-in-the-loop carried out with the RTDS simulator and a TI F28335 control card. The comparison shows that the new feature of the control algorithm, namely the delay compensation, improves the performance of the controller while the limited processing power deteriorates its optimal operation.

The presence of flexible ac transmission system (FACTS) controllers in transmission lines causes mal-operation of distance relays. The series-shunt FACTS devices have larger influence on the performance of the relays compare to the other FACTS controllers. Furthermore, high-resistance fault is another factor that relay become under-reach and cannot correctly identify the fault. In this paper, a method is provided based on synchrophasors to eliminate the effects of unified power-flow controller (UPFC) and fault resistance on the distance relay. In the presented method, the data of voltage and current signals of buses will be sent to system protection center (SPC). In SPC, an algorithm is provided based on active power calculation in buses which is able to eliminate the effects of both mentioned factors. The main advantage of the proposed method, in addition to the simplicity of the algorithm, is the ability to operate in all types of faults and in high-resistance faults. Furthermore, a technique is presented in this paper to calculate UPFC data. A comparison has been performed between this technique and another method where UPFC data is directly transmitted to SPC by communication channel. In modeling of UPFC, detail model is used based on 48-pulses voltage source converters. 1 2017 Elsevier Ltd Scopus

This paper tackles the technical and economical value of island interconnection links in remote island power systems. For this purpose, a novel deterministic hourly unit commitment on a weekly basis is formulated including the possibility of interconnection links between islands. The unit commitment reflects the common practice of the majority of real island power system operators when operating their systems: the economic dispatch is constrained in order to cover the loss of any on-line generating unit and the loss of any interconnection link between islands. Several islands of the Spanish Balearic and Canary archipelagos are used as illustrative real cases to assess the impact of existing and projected links between islands. The paper shows on one hand how reserve constraints drive the economical operation of real island power system. On the other hand, how the use of interconnection links not only enable the flow of cheaper generation power between islands, but also significantly contribute to the fulfillment of reserve constraints which translates into a cheaper and more sustainable island operation.

Since wind output power shows obvious fluctuations characteristics, direct grid-connection will bring great challenges to the safe and stable operation of the power system. In order to achieve better economic benefits, this paper adopts the dual-battery energy storage system (DBESS) operation mode which performs charge–discharge tasks separately considering the discharge depth and frequent charge–discharge conversion on battery life. However, the extreme operation will occur when considering the imbalance of charge and discharge energy of DBESS, which will make it difficult for DBESS to maintain long-term stable operation to smooth the wind output power. Besides this paper proposes a charge–discharge operational stability index for characterizing the system operation status, based on the real-time operating conditions of the energy storage system and wind power fluctuations. To ensure the DBESS long-term operation stability, the extreme operation problems caused by charge–discharge unbalanced energy in the long-term operating process must be solved. This paper used two proposed indicators as input factors and proposed a control strategy to adaptively fine-tune the first-order low-pass filtering time constant, to charge the battery throughput power in real-time and optimize two battery packs’ State of Charge (SOC). The effectiveness of the proposed control strategy is verified from two aspects by using MATLAB/Simulink software: the SOC optimizing control effect and the power fluctuation smoothing effect.

Hybrid HVDC system, which consists of the advantages of line commutated converter (LCC) and voltage source converter (VSC), is an emerging power transmission system. This paper presents a review of four LCC-VSC hybrid HVDC topologies. The first topology is the pole-hybrid HVDC system, in which the LCC and VSC form the positive- and negative-pole respectively. The second one is the terminal-hybrid HVDC system; in this topology one terminal adopts LCC and the other terminal adopts VSC. The series converter-hybrid HVDC system is the third topology wherein each terminal is formed by LCC and VSC in series. The fourth hybrid topology under consideration is a parallel converter-hybrid HVDC system with LCC and VSC connected in parallel in each terminal. The main contribution of this paper is a comprehensive analysis and comparison of the four mentioned hybrid topologies in terms of PQ operating zone, power flow reversal method, and DC fault ride-through strategy.

This paper introduces a day-ahead clearing model for the simultaneous energy and ancillary services market in high penetration of renewable energy sources (RESs) considering fast-start generators’ behaviors as non-spinning reserve providers. For managing the uncertainties of RESs, the system operator takes the advantages of the stochastic model of security-constrained unit commitment (SCUC) problem considering plausible scenarios. The non-convexities due to the startup costs, minimum power outputs, and commitment variables make the stochastic SCUC problem and market-clearing non-convex. Meanwhile, with traditional market-clearing methods for energy and ancillary services based on marginal costs or dual variables, the net profit of some generators may become negative. In this situation, more financial losses will be imposed on fast-start generators since they are marginal/intermittent units for managing the uncertainties of RESs. Using a duality gap minimization and profit insurance, this paper presents a pricing model to solve these issues. The proposed market-clearing model leads to uniform prices for ancillary services and nodal prices for energy guaranteeing the non-negative net profit of all generators. The results of the 3-buses system and IEEE 118-buses system are analyzed to illustrate the applications of the proposed pricing approach.

Weather based electrical power outages cover a huge part of consumer interruptions. So, reliable-economical operation of grids during extreme weather conditions is one of challenges for grid operators. In this regard, this paper proposes resilience enhancement programs in order to increase resilience and economic profits in a smart grid. In proposed approach, resilience improvement is done by modeling weather effects on branches outages and then re-scheduling of distributed energy resources and energy storages, load shifting and dynamic reconfiguration of distribution network. In this paper, hourly variation of weather depended failure probabilities are considered. Resilience enhancement programs aim to mitigate effects of events which may cause by extreme weather before fault inception by rescheduling of resources and selecting suitable reconfigurations. Also, reconfiguration isolates damaged parts after fault inception. The objectives in the proposed approach are defined as minimizing operational cost of distribution network and energy not supplied penalty costs from the system operator’s viewpoint, as well as, maximizing benefits of energy resources owners by considering weather conditions. A multi-objective optimization algorithm based on genetic algorithm and epsilon constraint method using fuzzy decision maker is employed to choose the best solution from a provided Pareto optimal set. In order to evaluate performance of proposed resilience enhancement programs and its effect, resilience assessment metrics are studied. Various simulations prove the efficiency of proposed model in compare with traditional grid during extreme weather conditions.

Nowadays, transformers can be operated close to their thermal limits. The state of the art in transformer thermal limits are Dynamic Thermal Rating (DTR). DTR can be based on two temperature limits: either a design temperature or maximal-permissible temperature. However, many papers estimate DTR with the design temperature only. Therefore, the true DTR is still underestimated since temperature limit is ignored as well as current limits (in some papers). Moreover, many papers rely on the conservative assumption of typical historical load profile or net load profile (considering the distributed generation, storage, electric vehicles). However, modern DSO can control the shape of (net) load profile. This can make a DTR estimation, assuming some typical (net) load profile, valuable only for a particular shape of (net) load profile but not for its modifications. This paper suggests a DTR feasible region which is constructed from current and temperature limitations and does not rely on typical load profiles. As a case study, we investigate DTR in cold and warm climates: one case in Russia with a continental climate and another in France with a temperate climate. In these climates, we assess DTR for the most common combinations of current and temperature limitations, used in standards and literature. As a result, DTR can ensure an additional capacity up to 45%. DTR, based on temperature limits, are 100% of time higher than nominal rating. Moreover, the limiting factors of DTR are quantified based on analysis of 34-year ambient temperature data. Finally, comprehensive recommendations for transformer overloading are formulated.

This paper presents a mixed-integer linear programming model for volt-var optimization considering the chronological operation of distribution systems containing distributed energy resources (DERs). The proposed model describes the operation problem of capacitor banks (CBs) and voltage regulators (VRs), and it is further based on the steady-state operation during each time interval contained in typical scenarios of distribution systems. A procedure using a K-means clustering algorithm is used to select the scenarios, thus preserving the simultaneity and chronological combination of different loads and DERs. According to the formulation that we developed, the regulation devices become sensitive to downstream load variations, since we use explicit current variables to control automatic CBs, and since we include means to compensate voltage drops along distribution lines in the control of VRs. The model is validated by comparing the results obtained during several tests of two typical cases with those obtained through nonlinear power flow. The typical case studies presented in the paper highlight the good agreement between the results obtained with the linearized model and with power flow method; further, the practical results confirm that the use of typical scenarios allows representing different levels of loads and DERs, while keeping the validity and performance of the proposed model.

In this paper, we present a trading-oriented battery energy storage system (BESS) planning model for a distribution market. The proposed planning model is formulated as a mutual-iteration and multi-objective two-stage optimization problem. The first stage is designed to optimize the internal resources allocation including PV system, wind turbine, gas turbine and BESS. Then a double-side auction model named as averaging pricing market (APM) mechanism is presented in the second stage, which aims at solving the issue of energy trading benefit loss caused by the relatively high difference between time-of-use (ToU) electricity price and feed-in-tariff. The contribution of this paper lies in combining the planning of BESS with the optimization of the distribution electricity market. Numerical results on a modified IEEE 24-bus system are presented to demonstrate the superiority of the two-stage optimization model and the sensitivity of the model is also investigated based on regret analysis.

This paper presents a coordinated control strategy based on direct Lyapunov theory to handle the consistency of AC grids in a multi-terminal (MT) modular multilevel converter (MMC)-HVDC systems during varying both loads and DC link voltage. As the first contribution, a set of dynamic equations is proposed based on separating the dynamics of MMCs upper and lower arms state variables. The dynamics consists of only their related upper/lower arms state variables leading to more effective components for the steady state terms of proposed control technique. To develop the dynamic parts of the controller, the global asymptotical stability of MT MMC-HVDC system is assessed by direct Lyapunov method. As another advantage of the separated dynamic equations, the Lyapunov theory is able to exploit very simple decoupled components for the dynamic parts of proposed control functions. Moreover, in order to specify the variation trend of Lyapunov coefficients, further stability analysis contributes to demonstrating the effects of Lyapunov coefficients on the MMCs state variable errors and its dynamic. As another main contribution of this paper, two independent capability curves based on the power injection capability of the MMCs upper and lower arms, are obtained which will be assessed through changing the input and output voltages as well as MMC parameters. Finally, simulation results in MATLAB software are utilized to verify the validity of proposed control strategy.

Natural incidents with a low occurrence probability and high impacts like windstorms and earthquakes, threaten the optimal operation of the distribution networks. Hence, some operational solutions are required to improve network resiliency. Microgrids (MGs) provide a proper solution for the resiliency enhancement of distribution networks to reach the optimal resilient operation under such incidents. Also, the inherent uncertainties make the resilience-oriented optimal operation of MGs more critical and should be modelled by efficient uncertainty modelling tools. In this paper, a hybrid stochastic-robust optimization (HSRO) approach is used to get the optimal scheduling of an MG under normal and resilient operation mode. The impact of the upstream grid price uncertainty on the optimal scheduling of MGs is modelled using the robust optimization approach. Other dominant uncertainties, including the wind power, photovoltaic (PV) power, and active/reactive electrical loads, are also modelled by stochastic optimization via generating a set of relevant scenarios. In addition, adjustable and interruptible demand response programs (DRP) are implemented to improve the resilient operation of MG under incidents. The proposed approach tries to improve the MG operation under uncertainties during the resilience and normal mode of network. Furthermore, this paper concentrates on the robust and normal strategies adopted for the resilience-oriented scheduling of MGs. The effectiveness of the proposed framework is evaluated through a large-scale MG test system.

This paper investigates the distributed energy management problem of integrated local energy systems (ILESs). Firstly, this paper proposes a non-convex energy management model of ILES, which considers transmission loss between multi-energy devices and treats the ILES component units (including demand response) as independent decision-making entities. Secondly, we propose a neurodynamic-based optimization algorithm to solve the problem, which can properly handle with the inseparable inequality constraint. The proposed distributed neurodynamic-based algorithm only requires the information exchange among neighbor nodes and offers less computation, lower communication burden and faster convergence compared with some traditional centralized methods. Finally, two simulation results are provided to illustrate the effectiveness of the proposed approach.

In the context of the rapid development of the energy internet, the traditional power demand response has gradually expanded to an integrated demand response. Multiple energy sources such as electricity, gas, heating, and cooling, are coupled and coordinated in integrated energy system, which is conducive to improving comprehensive energy utilization efficiency and providing more opportunities for the demand side to participate in system optimization. This paper focuses on the optimal operation of community integrated energy system (CIES). From the perspective of demand response, the horizontal complementary substitution and vertical time shift strategy of electric-gas-heating-cooling are introduced based on the interactive response characteristics analysis of multiple energy loads, and the cooperative complementarity and flexible transformation of multiple energy sources are taken into account to establish a stochastic robust optimal operation model of CIES based on integrated demand response. In this paper, a mixed-integer linear programming method is applied to solve the model, and the simulation results show that the proposed method can effectively reduce the operation costs and promote a balance between energy supply and demand while further tapping the potential of the energy demand side and achieving the economical, flexible and efficient operation of community integrated energy system.

The ongoing increasing demand for electrical energy and the reliable access to remote renewable energy generation points such as off-shore wind power stations or solar power generation, have revived the interest in HVDC multi-terminal systems. With the expansion of HVDC systems, their reliability assessment has become continuously important. One of the most important aspects of the reliability assessment of electrical systems is to have an accurate understanding of the engineering implications of the system and its associated components. The next step is to appropriately model the system components to represent their characteristics and functions in the overall system. In order to deliver a specified amount of electrical power, different methods can be considered. There are two general methods: 1) using several electrical power sources with low capacity, or 2) using fewer electrical power sources with high capacity. The decision to choose a certain method for transmitting electrical power depends on various parameters, and one of the most important parameters is reliability studies. This paper presents reliability modeling and analysis of HVDC transmission systems incorporating hybrid HVDC breakers to interrupt HVDC line faults. In this paper, comprehensive detailed reliability models of point-to-point, three-terminal, and four-terminal configurations are developed. Using this equivalent reliability model, various reliability indices are calculated at the load point of the system. Also, the effects of line constraints and incorporating DC circuit breakers of the DC transmission lines on the load point reliability indices have been evaluated. In the end, the effect of the load level on the reliability indices of the load point of each configuration has been assessed.

Application of Dynamic Thermal Rating (DTR) for optimal utilization of transmission system components has been addressed in several studies. For Offshore Wind Power Plants (OWPPs), as a consequence of intermittent wind generation combined with favorable ambient conditions and strict design regulations, power transformers tend to be over-dimensioned. Experience suggests that transformer lifetime exceeds OWPP life by tens of years, therefore DTR-based design of transformers can facilitate the optimization of OWPP export system. The recent breakthroughs in Thermo-Electric Equivalent (TEE) and aging models of transformers suggest that temporal development of Top Oil (TOT) and Hot Spot (HST) temperatures can be calculated efficiently, which when combined with monitoring of moisture and oxygen development can accurately assess the transformer health in real-time using Degree of Polymerization (DP). However, the economic optimization of offshore windfarms substations using DTR and lifetime utilization-based transformer design during the OWPP planning phase has not been addressed in the literature due to the risks involved in balancing system reliability with windfarm profitability. In this paper, a novel strategy is proposed to utilize transformer DTR for cost optimization of offshore windfarm export system during the OWPP design phase. Net Present Value (NPV) is used to assess capital cost impacts on transformer and Offshore Substation (OSS) design in year 0, and yearly revenue change over OWPP lifetime due to increased losses and possible energy curtailment as a result of smaller transformers. The unique methodology accounts for varying wind turbine availability due to maintenance and concurs with OWPP design requirements for OSS transformer contingency by generating statistical scenarios based on distribution functions and Markov Chain (MC) models respectively. Moreover, the MC models are adapted for two fundamentally different design concepts for OWPP export system: n-1 contingent and non-contingent. The implementation of probabilistic Markov models to resolve stressful periods with increased transformer loading is distinctive to this paper and offers a unique perspective for DTR-based transformer size optimization. Long-term site assessment data for OWPP including historical wind generation is used for transformer thermal assessment (TOT and HST) based on its load and ambient conditions. Furthermore, the reliability of design for varying construction and commissioning conditions is ensured by tracking DP-based transformer lifetime utilization over the operation period for three scenarios of oxygen and moisture development: conservative, high moisture and high oxygen. The case study of a 1200 MW offshore windfarm with two 220 kV parallel export circuits and four 33/220 kV transformers in one OSS located 50 km off the east coast of UK has been used in this paper. 100 scenarios of wind turbine availability and transformer contingency are simulated and the optimal design is determined for the worst-case scenario. Results indicate that optimal transformer rating can result in high thermal stress and minor energy curtailment during extreme contingency conditions for both the design concepts. However, the reduction in transformer size to align with OWPP operation life can reliably improve the windfarm business case significantly, particularly for the n-1 contingent design concept with conservative transformer moisture and oxygen conditions.

Electrical Transmission System Operators (TSO) are trusted with ensuring the safety and reliability of transmission infrastructure which can span thousands of kilometers. Maintenance of such a geographically expansive system is naturally a matter of concern and companies invest heavily in tracking infrastructure state which still relies predominantly on visual inspection. This paper presents an automated condition assessment methodology for concrete poles supporting overhead conductors based on deep learning object detection networks. Nine defect conditions ranging from incipient to severe are automatically detected from infrastructure photographs and mapped onto established Health Indices used by maintenance personnel. Three different deep learning networks are tested and new metrics, specific to this problem, are defined to evaluate their performance based on asset Health Index (HI) values. Results indicate that deep learning object detection networks hold promise for significantly reducing manual labour associated with visual inspection, especially when combining with automatic asset identification based on image geotag. This paper shows acceptable performance on more severe defect types.

Convex relaxations of the AC Optimal Power Flow (OPF) problem are essential not only for identifying the globally optimal solution but also for enabling the use of OPF formulations in Bilevel Programming and Mathematical Programs with Equilibrium Constraints (MPEC), which are required for solving problems such as the coordination between transmission and distribution system operator (TSO/DSO) or optimal network investment. Focusing on active distribution grids and radial networks, this paper introduces a framework that collects and compares, for the first time to our knowledge, the performance of the most promising convex OPF formulations for practical applications. Our goal is to establish a solid basis that will inform the selection of the most appropriate algorithm for different applications. This paper (i) introduces a unified mathematical and simulation framework, (ii) extends existing methods to retain exactness in a wider number of cases and (iii) consider reactive power injections. We conduct simulations on the IEEE 34 and 123 radial test feeders with distributed energy resources (DERs), using yearly solar irradiation and load data.

This paper presents a data-driven control approach for maximizing the total power generation of the offshore wind farm by using a recently developed learning model predictive control (LMPC) algorithm. The control is designed by coordinating yaw angle control actions of wind turbines to mitigate the wake interactions among the turbines for increasing the total farm power production, which is termed as wake redirection. This paper mainly focuses on designing the architecture and methodology of the LMPC for wind farm, including a unified wind turbine wake interaction model, the LMPC for minimizing an iteration cost function, the recursive feasibility, stability and convergence analysis. Extensive comparative studies are conducted to verify the performance of the LMPC in comparison with the existing model predictive control (MPC) method under the same wind speed conditions. The results show that the wind farm yields up to 15% more power production by using the LMPC than the conventional MPC.

To realize real-time wind farm output power regulation with power-sharing among storage devices that have different state of charges (SoCs), this paper proposes the cooperative control of the distributed wind-storage combined systems (WSCSs). It firstly establishes the mathematical model of doubly-fed induction generator (DFIG) and hybrid energy storage system (HESS) and implements the controls for two devices, respectively. Secondly, based on the power consensus protocol, an active power cooperative control strategy of the distributed WSCSs is proposed. It ensures that the energy storage devices can cooperatively compensate for wind power according to their different capacities to regulate the output power of the wind farm (WF) in real-time. At the same time, the high- and low- frequency power fluctuations can be effectively shared by the supercapacitors (SCs) and batteries, overcoming the deficiencies of the conventional battery energy storage system (BESS). Then, considering that the SoCs of the batteries are not identical, this paper further proposes a DFIG grid-side reference power adjustment strategy based on consensus protocol. It guarantees that each HESS can effectively achieve power-sharing while regulating the output power according to the SoCs of batteries. Finally, simulations in PSCAD/EMTDC under various scenarios are conducted to demonstrate the effectiveness of the proposed control strategy.

The number of fully-rated converter wind turbine generators equipped with a Permanent Magnet Synchronous Generator (PMSG) has sensibly increased in the late years. The optimal utilization and controllability of a PMSG is achieved using a Pulse Width Modulation – Voltage Source Converter (PWM-VSC) that allows interfacing the electric machine with the distribution network by means of a DC link. The generator/Machine-Side Converter (MSC) controls the operation of the PMSG. This paper proposes a methodology to assess the feasibility of the steady-state working points of PMSG based wind turbines without running a set of time-consuming time-domain simulations. Three control objectives for MSC are usually considered: stator voltage control, unitary power factor control and torque control. The aim of the present paper is that of providing an analytical methodology to check the feasibility of the steady-state working points dictated by different control strategies of the MSC starting from the specific operational constraints provided by the wind power plants characteristics. The effectiveness of the proposed methodology is demonstrated comparing numerical results of a MATLAB® implementation against dedicated time-domain simulations. Simulations results demonstrated the appropriate performance of the proposed methodology characterized by the almost negligible computational time required.

The research “Behavior Change and Energy Use” (US Department of Energy and Climate Change, 2011) [1] shows that with better information in the monthly electricity bill, the Energy Performance Certificate (EPC) can encourage people to reduce their energy usage. That is why smart meters - the emerging technology to help people to know their monthly energy consumption, are gradually replacing mechanical power meters. In this paper, we investigate a special energy monitoring process named Non-Intrusive Appliance Load Monitoring (NIALM), which is potentially the best method to give consumers pertinent information with respect to power consumption. However, real-time feedback feature in a low cost NIALM system is still a big challenge in such technology because of the complication in NIALM’s algorithms. System on Chip (SoC) technology can solve this challenge. Besides including high-speed interconnection and multi-processors, integrating Field-Programmable Gate Array (FPGA) into SoCs may be the most important evolution, which provides developers a powerful tool to develop a low cost but high performance system. Therefore, in this paper we proposed a development of a real-time NIALM system based on the SoC with FPGA acceleration.

This paper presents a new approach to the design of robust proportional-integral (PI) load frequency controller (LFC) with overlapping structure in the presence of generation rate constraint (GRC) and time-varying communication delays. The power system is decomposed into overlapping areas with tie-lines being the overlapping (shared) parts between the areas. The overlapping LFC design is based on the expansion-contraction principles, where the power system is first expanded in such a way as to decouple the areas from each other. Then, for obtaining an appropriate level of performance, robust local stabilising PI controllers are designed for each of the decoupled areas using an iterative linear matrix inequality (ILMI) algorithm such that asymptotic stability with a minimal H ∞ performance level is achieved. Finally, the designed robust local H ∞ controllers are contracted (transformed) to a robust overlapping LFC for implementation on the multi-area power system. Although the approach is applicable to multi-area power systems, a time-delay three-area interconnected power system experiencing communication delays is considered in this paper. Simulation results under different scenarios clearly illustrate an improved performance obtained with the overlapping structured LFC of this paper compared with existing decentralized LFCs.

In a deregulated power system, incentive regulations for network owners are designed to direct the network investment. An innovative method that assesses the incentive regulation in distribution networks is proposed in this paper. The method quantifies the interplay between incentive regulation, network investment, and network performances. It allows regulators and the distribution system operators (DSOs) evaluating the economic effects of investments within the incentive regulation framework. Considered network investments include the investment in network infrastructure and performance improving. The assessment is based on a network investment optimization model considering multi-period optimal power flow and regulatory constraints. The main contributions of this paper include the modeling of the incentive regulations and the quantification of the impacts of incentive regulation on network infrastructure investment and performance improving investment.

This paper focuses on improving the dynamic response of autonomous microgrids (MGs) by proposing a grid-forming inverter controlled as a virtual synchronous generator (VSG), in combination with a supercapacitor (SC)-based energy storage system (ESS). By this arrangement, the MG-forming VSG is designed to react only in transitory regimes, the steady-state load being distributed to other MG-supporting inverters spread within the MG. In this way, the MG-forming VSG can maintain its full power reserve capacity for dynamic response. The paper details the control solution for the MG-forming inverter, including the VSG and SC-ESS control. The control method for the MG-supporting inverters that allow achieving the proposed control approach is also described. To prove the concept, the paper includes simulation results and experiments accomplished on a complex laboratory MG system based on three parallel inverters, one being controlled as MG-forming VSG, while the others operating as MG-supporting inverters.

This paper presents a decision support system (DSS) for multi-objective dispatch of an electric microgrid considering cost of operation, peak load reduction, and emissions. Discrete compromise programming (DCP) is used as the multi-criteria decision analysis (MCDA) technique for providing the decision support to the distribution system operator (DSO) by ranking various alternatives based on preference of the objectives. The focus of this paper is the application of DCP-based MCDA to select feasible dispatch solutions closest to the preference of the DSO when multiple objectives are considered in the microgrid dispatch. This technique obtains non-dominated solutions in case of conflicting objectives without generating a Pareto front, and hence avoiding prohibitive computational cost. DCP can be used for MCDA in both exact and metaheuristic dispatch algorithms. Uncertainty in renewable energy forecasting and load demand is included through a scenario-based approach by sampling empirical distributions. The dispatch algorithm considers a two hour look-ahead time horizon. Simulations are performed on a notional electric microgrid with diesel generator, solar photovoltaic, and energy storage. The microgrid is based on the IEEE 13-node test feeder and the results are presented.

This paper presents a methodology for the testing of intelligent electronic devices (IEDs) in IEC 61850-based substation automation systems (SAS), without any service interruption to the end users, during the testing process. IEC 61850-based SAS provides opportunities for a new level of substation operation, enhancing operating efficiency, and protection reliability based on Ethernet-based communication networks. Due to these changes to the conventional substations, importance and dependency on IEDs is increasing rapidly. Frequency of faults occurring in the power systems is random in nature and is unpredictable. It is necessary to test and maintain the IEDs to ensure their proper operation at crucial times. This paper presents a novel methodology for testing the IEDs, without compromising the service reliability during the testing process. The proposed methodology exploits the inherit characteristics of IEC 61850 standard i.e. Network - based communication, interchangeability between multivendor IEDs, and engineering based on substation configuration language (SCL) files. The feasibility and robustness of the proposed method has been verified by performing several performance verification tests with different performance metrics.

The harmonic currents injected by high speed electric trains cause not only the pollution of power quality (PQ), but can also excite harmonic resonance in the traction power supply system (TPSS) of high speed railway (HSR). It is essential to build a proper harmonic distribution and prediction model for high speed electric trains. This paper presents an approach to obtain the probability density of harmonic current based on a non-parametric Kernel density estimation method, which can describe the probability distribution characteristics using a data-driven mechanism. A prediction algorithm for harmonic current based on confidence intervals, which can simulate harmonic current injection using a continuous sampling method, is presented in this paper. The new high speed CRH380AL electric multiple units (EMU) are used to illustrate the method. The results show that the proposed approach and algorithm can be useful for harmonic source simulation in harmonic power flow calculation and PQ assessment.

The presence of power electronic-based wind turbines and photovoltaic systems in distribution networks has provided distribution companies an opportunity to implement voltage control through using the reactive power of these systems. In this paper, a decentralized method based on fuzzy logic is proposed to control the reactive power of distributed generations (DGs) regarding the technical constraints. The fuzzy system is optimized by gradient descent algorithm (GDA) and then implemented on various DG technologies including a photovoltaic (PV) system, permanent magnet synchronous generator (PMSG) wind turbine and also a doubly fed induction generator (DFIG) wind turbine. The system under study is tied to a real distribution network. Having simulated the system, the paper shows that the fuzzy system can appropriately determine the desired reactive power that should be produced by each DG based on the voltage variation of the bus at which the DG is connected. Furthermore, a centralized voltage control is also applied to the same network to verify the performance of the method proposed. The verification indicates that the method is capable of finding the near-optimal solution. A scenario in which an unwanted conflict appears in the DGs’ function is defined in detail and then a strategy is presented to resolve the situation. In addition to this, the coordination between the stator of the wind turbines and grid side converter (GSC) is examined. To investigate the robustness of the proposed method in different distribution networks, simulation results are also presented for IEEE 33-bus distribution test system. The numerical results show that the fuzzy system can effectively control the voltage of the DG connection bus.

Integration of wind power generation into power distribution grid may result in stability and power quality challenges. In this way, this paper first reviews the grid integration issues of wind power generation. Then, as the main contribution, the paper develops in-depth theoretical analyses for examination the grid integration issues such as voltage rise, output power fluctuations, and static and dynamic voltage variations created due to grid integration of wind turbines and corresponding aerodynamic power fluctuations. Besides, the paper addresses these issues by proposing elaborate control approaches and by using the static compensator (STATCOM). The paper first addresses the problem of voltage rise by active power curtailment through pitch control. However, this method may result in a large waste of wind energy that may not be cost effective. Next, the STATCOM is implemented and efficient control approaches are proposed for addressing the problem of voltage rise, improvement of voltage profile and suppression of voltage and power fluctuations. At the end, simulation studies for the study distribution grid are given. The study system is an actual distribution grid in Iran with approximately 7.9 MW wind power generation and 21.9 MW load.

This paper focuses on material that is required for the practical application of fuzzy reliability; impacts of repair and data uncertainty. For this purpose, a holistic solution is presented to measure and manage the degree of uncertainty in the reliability indices of repairable systems. First, a flexible form of Markov Processes (MPs) namely Equivalent Transition Rate (ETR) is introduced in order to obtain an analytical formalism. Second, we delve deeply into the issues of fuzzy arithmetic by providing an intuitive, methodic and computational discussion of feasible approaches; Standard Fuzzy Arithmetic (SFA), Intermediate Variables (IMVs) and Taylor Series (TS). Specific emphasis is then placed upon Fuzzy Transformation Method (FTM). Third, we evaluate the performance of proposed techniques in case of a reliability problem involving fuzzy complexity. Consequently, the reader is given useful insights into the nature of fuzzy computations and practical impediments when measuring uncertain data. Given materials and methods, current paper provides a broad perspective by evaluating the reliability characteristics of a repairable Substation Automation System (SAS) that is one of the fundamental elements in Smart Grids (SGs). In this regard, quantitative results and diagrammatic outputs are supported by a detailed analysis under different assumptions. Our findings demonstrate that fuzzy outputs are computationally tractable and properly estimated. Besides, this investigation shows how the objective SAS can respond to the variations in reliability data. The framework proposed here encourages the continuation of the work towards more applications in electric power components and systems. As far as we know, there are no studies on these techniques, results and remarks.

The main objective of this paper that distinguishes it from other similar articles is to employ predictive control strategy to improve the stability of power systems (4- machines and 10-machine) in presence of wind farms based on Doubly Fed Induction Generator (DFIG), using Static Synchronous Series Compensator (SSSC) and Super Capacitor Energy Storage System (SCESS). In this paper, SCESS is used to control the active power in the Grid Side Convertor (GSC) and SSSC is employed to reduce low frequency oscillations. The proposed strategy based on the predictive control can be simultaneously used to control the active and reactive power of the Rotor Side Convertor (RSC) as well as damping controller design for SCESS and SSSC. A function is used in the predictive control strategy to reduce computational complexity in selecting the input paths of Laguerre functions. Moreover, the sampling time is reduced by means of employing the exponential data weighting. Simulation results for the function-based predictive control using disturbance scenario in the field of non-linear time are compared with the other two methods, model-based predictive control and classic model (without using the predictive control). The effectiveness of the proposed strategy in improving stability is confirmed through simulation result.

This paper proposes the application of fictitious reference iterative tuning (FRIT) method to optimize the gains of dc voltage controller of grid connected photo-voltaic (PV) system. It may be difficult to achieve good control of dc voltage using conventional PI controller having only one-degree-of-freedom (1-DOF) due to the trade-off between overshoot (in step response) and disturbance response. In this paper, the optimal control of dc voltage is proposed with improved disturbance response by implementing 2-DOF PI controller structure. FRIT method has been programmed in MATLAB based upon the particle swarm optimization (PSO) algorithm. The fundamental idea related to FRIT method is the extraction of input and output data, reference model setting and range of controller gains. The performance of dc voltage control for the optimized 2-DOF PI controller is also compared with the fuzzy logic controller (FLC) response.

Transmission systems, connection of distributed generation to the grid are increased due to increase in power demands. This fact causes the increase in short circuit level of power networks. The occurrence of fault in such networks leads to large short circuit currents throughout the system, which may exceed the rating of existing circuit breakers and can damage system equipment. There are some approaches to reduce this fault current such as power network reinforcement and utilization of fault current limiter (FCLs) in power systems. Power system reinforcement is too difficult if not impractical. Therefore, the utilization of FCLs can provide an effective way to suppress fault currents. The effectiveness of FCL depends on the number of FCLs and their installation location. In this paper, a novel approach is presented to determine the optimal number and location of FCLs to improve the power network reliability and fault current reduction based on different conflicting objective functions. IEEE 39 BUS system and IEEE 57 BUS system are considered to evaluate the effectiveness and feasibility of the proposed method. The objective functions considered for the optimal allocation are reliability of power system, economic impact and short circuit current reduction. Unlike what has been previously done in literature, in this paper Pareto based optimization algorithms, namely non-dominated sorting algorithm, multiobjective particle swarm optimization and multiobjective evolutionary algorithm based on decomposition, are utilized to deal with this problem. The use of these methods made it possible to obtain the Pareto optimal front in which these objective functions are optimized simultaneously.

Under the thermal storage strategies in present studies, the power generation unit (PGU) in combined cooling, heating, and power (CCHP) system is turned on/off only based on the state of storage tank; or the operation state of PGU is determined by thermal demand or/and electric demand, which has no relationship with the state of storage tank. For these traditional strategies, either energy demands or state of storage tank is considered for the operation of system. This paper proposes a novel thermal storage strategy (TSS2) for CCHP systems, which determines the operational state of the PGU based upon electric demand, thermal demand and the state of thermal storage device. In this paper, the operation principle of TSS2 is described and compared with the traditional strategies. A hospital in Shanghai was used to evaluate and compared the performance of the system operating under different storage strategies. The results show that TSS2 improves the performance of CCHP system more significantly compared with the traditional strategies. TSS2 can make both PGU and capacity of thermal storage tank be fully used by CCHP system to produce more electric and thermal energy to meet demands of building. Compared with system without thermal storage, the minimum and maximum increase rates of annual total cost saving for traditional strategies are 0% and 14.84%, respectively. However, for TSS2, they can reach 3.79% and 20.22%. Similar conclusions could be achieved for CO2 emission reduction and primary energy saving. The effect of TSS2 on the CCHP system performance is different under different operation strategies.

This paper is proposed to establish a new control algorithm for UPQC (unified power quality conditioner) to improve power quality and manage effectively equal reactive power sharing between shunt and series inverter of UPQC under unbalanced source voltage condition. The extraction of instantaneous power angle for reactive power sharing faces difficulty with unbalanced source voltage condition. This paper presents a new SRF (synchronous reference frame) based PAC (power angle control) method using decoupled load current parameters for efficient utilization and coordination of UPQC inverters. The proposed controller contributes in improvement of source current and load voltage harmonic profile, provides efficient way of load reactive power compensation and load voltage compensation for sag, swell and unbalanced condition. Effect of source voltage variations in the form of sag, swell or unbalancing on variable power angle estimation and reactive power calculations are also validated through a mathematical analysis. SRF based PAC control approach and PAC based UVT (unit vector template) control approach is adapted for estimating the reference signals of shunt and series inverter respectively and thus reducing the need of extra computation. The simulation and experimental analysis is carried out using Matlab/Simulink software package for computer simulation and a dSPACE based experimental setup for real time verifications.

Electric Vehicles (EVs) have been suggested as alternatives to conventional vehicles for reducing petrol consumption and carbon dioxide (CO 2 ) emissions. When a large number of EVs connect to the grid, they can cause a large amount of power loss. Where to install multiple charge stations in the grid, so as to mitigate losses caused by EVs when providing energy to those EVs, is becoming vitally important. In this paper, a distribution test-network model is described. A new analytical method is proposed, using the stations’ cooperation in terms of optimal active and reactive power dispatch as well as power flow analysis for locating the optimal placement of charge stations, so as to reduce power losses. This method is compared with the previously developed current density method for single charge stations using system simulation results. It was demonstrated that the methods proposed in this paper are more accurate than the current density method, and that 17% of the average active power loss can be saved for three different types of load profile. In addition, 27% of the average active power loss was saved by installing two charge stations rather than no charge stations in the test-line. It is shown that this could represent a 2.6% annual yield above inflation for investing in installing and running such charge stations.

This paper considers a three-phase, low voltage community network with both grid-connected and autonomous modes of operation, which is composed of a group of residential houses and some single-phase, converter-interfaced distributed energy resources (DER) with equal and arbitrarily scattering of the DERs among the phases. The vigorous operation of such a microgrid system is examined with the proposed management techniques. In such a network, it is highly probable for one phase to have a high generation capacity while the other phases experience a higher demand; a technically challenging problem for a network operating in autonomous mode. In this paper, it is proposed for the single-phase DERs of such a system to operate under a droop-based voltage control technique while an appropriate technique is proposed to facilitate the transmission of the excess power from one phase to other phases. The proposals are validated by extensive digital simulations in PSCAD/EMTDC for several scenarios to demonstrate the feasibility of operating such a system and the efficacy of the proposed techniques.

This paper discusses the losses analysis of low power high-frequency Wireless Power Transfer Systems (WPTSs). Ideal models for efficiency evaluation of WPTS can predict performances that are quite far from the real ones. The model proposed in this paper includes semiconductor devices losses, as well as modulation of duty-cycle and phase for the secondary side rectifier. The global influence of semiconductor devices and control parameters on the overall WPTS performances is numerically determined by solving the herein discussed non linear equations system. Experimental measurements realized on a 2 W@6.78 MHz WPTS demonstrate the validity of this analysis.

This paper describes the overall dynamic models of tidal current turbine driving either a doubly fed induction generator (DFIG) or a direct drive permanent magnet synchronous generator (DDPMSG) connected to a single machine infinite bus system including controllers to improve system stability. Testing with ranges of controller gains is carried out to establish zones of stability. The overall results show the advantages of using the DDPMSG over the DFIG. In this paper we used two different models for each machine; the full model and the reduced order model. Also IEEE-9 bus system is used in this work for multi-machine system and tested for a small signal stability analysis.

The objective of this paper is to propose a simple transmission congestion pricing scheme based on tracing principle by considering generator fixed cost, cost for incurring loss and transmission congestion cost. Restructuring has brought about considerable changes by the virtue of which electricity is now a commodity and has converted into deregulated type. Such a competitive market has paved way for innumerable participants. This concept of restructuring has led to overloading of transmission lines. In this paper, power flow tracing has been employed by using suitable optimization algorithm, where the real power generation has been maximized. Congestion in the transmission line has been produced in a new fashion by maximizing the real power demand. The power flow under normal operating condition and congestion is determined and hence the difference in power flow is estimated. Based on the estimated power flow difference, the transmission line congestion cost is computed. Pool model and bilateral model has been considered in simulation study to introduce the concept of deregulation. The proposed method is tested and validated on Modified IEEE 30 bus test system and Indian utility 69 bus test system.

In this paper, STATCOM performance for voltage–reactive power control is investigated by comparing different tuning methods, used to evaluate gain parameters of STATCOM controller in presence of high probabilistic uncertainty in input wind power and reactive power load demand. To control voltage transient response in least time, reactive power demand is managed by STATCOM. The conventional methods for tuning gain parameters of STATCOM controller do not satisfactorily operate in case of random disturbances and therefore, advanced controllers such as Genetic Algorithm (GA), Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) are required. The main contribution of the paper is: (i) Investigation of STATCOM performance in presence of high probabilistic uncertainty with step changes in input wind power and reactive power load demand, (ii) system studies during dynamic conditions with composite load model in lieu of static load model in the system, (iii) comparison of voltage control and STATCOM reactive power using various tuning methods. Results comparison through all tuning methods show that advanced tuning methods are able to preserve optimal performances over wide range of disturbances using Integral of Square of Errors (ISE) criterion.

This paper proposes a novel strategy to forecast the short-term wind power using model structure selection in combined with data fusion technique. The available inputs are usually treated as an integrated one for the predictive modeling, which ignores the fusion of the different types of inputs. This results the relationship between the multi-inputs and desired outputs are not effectively reflected in forecasting procedure. Moreover, the performances of the various types of data fusion methods are susceptible to the wind power distribution and critical factors such as smoothness, overlapping, the intrinsic amount of information evaluation and the number of neighbor points. These outlined factors can result the lower generality ability of the proposed methods due to the insufficient optimization of the model structure. So this paper presents short-term wind power forecasting method using model structure selection technique in combined with four representative fusions of dimensionality reduction methods to optimize the model structure, promote the computational efficiency and improve the forecasting accuracy. Experimental evaluation based on the real data from the wind farm in Jiangsu province is given to verify the effectiveness of the proposed method by comparing the traditional techniques.

Power systems require a reliable supply and good power quality. The impact of power supply interruptions is well acknowledged and well quantified. However, a system may perform reliably without any interruptions but may have poor power quality. Although poor power quality has cost implications for all actors in the electrical power systems, only some users are aware of its impact. Power system operators are much attuned to the impact of low power quality on their equipment and have the appropriate monitoring systems in place. However, over recent years certain industries have come increasingly vulnerable to negative cost implications of poor power quality arising from changes in their load characteristics and load sensitivities, and therefore increasingly implement power quality monitoring and mitigation solutions. This paper reviews several historical studies which investigate the cost implications of poor power quality on industry. These surveys are largely focused on outages, whilst the impact of poor power quality such as harmonics, short interruptions, voltage dips and swells, and transients is less well studied and understood. This paper examines the difficulties in quantifying the costs of poor power quality, and uses the chi-squared method to determine the consequences for industry of power quality phenomenon using a case study of over 40 manufacturing and data centres in Ireland.

Minimization of the discrimination time between the backup and main overcurrent relays is one of the most important issues in the relays coordination of a power system. Finding the Time Setting Multipliers (TSMs) by the evolutionary algorithm has been investigated in the previous papers. In this paper, in addition to TSM, different characteristics of the overcurrent relays are considered to improve coordination. On the other hand, the coordination problem can be considered as an optimization problem, which can be solved by artificial intelligent methods. Recently, a novel optimization algorithm, Hyper-Sphere Search (HSS) algorithm, has been introduced. The HSS algorithm is adapted to the problem of this paper, in order to select the characteristics of the overcurrent relays and their TSMs. The result of HSS is compared with the genetic algorithm which is used in the previous studies. The simulation results on the sample network show the effectiveness of HSS and relays characteristics in terms of better coordination.

Post-deregulation era of power system operation produces more pressure on Independent System Operator (ISO) to ensure congestion free transmission network without destroying system security. In order to relieve transmission congestion, ISO initiates correct methods by maintaining system reliability and security. This paper considers a novel efficient technique for congestion management like generator real power rescheduling by integrating a pumped storage hydro unit (PSHU) in the system. This paper presents an optimization model for congestion management by incorporating the calculation of two factors like generator sensitivity factor (GSF) and bus sensitivity factor (BSF). Optimal location of PSHU is identified using the value of BSF’s and the numbers of participated generators for congestion management by rescheduling their outputs are determined using the value of GSF’s. The impact of PSHU has been investigated to manage transmission congestion which further reduces the congestion cost and improves security of the system. The proposed method for congestion management considering PSHU is tested on modified IEEE-39 bus New England test system and the validity is obtained by considering the same problem without the presence of PSHU. The result of proposed work with proper utilization of PSHU illustrates the impact of PSHU towards the congestion management and also exploration of firefly algorithm for minimizing the transmission congestion cost.