Search

• Pioneer Spain: first country to include in grid code the synthetic inertia capability. • Synthetic inertial response of a PV inverter studied based on the Spanish grid code. • Analyzed the role of power plant controller in the dynamic response of the facility. • PV system able to contribute to frequency control providing an inertial response. As the large-scale integration of renewable energy grows, inertia in power systems is reduced, which causes the rate of change of frequency to increase and worsens the frequency nadir during disturbances. There is, therefore, an urgent need to accelerate the development of international standards and regulations that require solar PV systems and other renewable installations to also contribute to stability in the network by emulating inertia. Spain is a pioneer in this field: it is currently the only country that includes the synthetic inertia technical requirement in its grid code. In this context, the present paper analyzes the synthetic inertial response of a PV system model that forms part of an actual PV power plant in Spain under two scenarios, according to its grid code: i) when measurements are conducted at the power conversion PV system level and hence only its control algorithms are executed; and ii) when measurements are conducted at the power plant level and the power plant controller also influences the response of the entire installation. Among its aims, this paper seeks to thoroughly assess the response of an actual PV power conversion system that has the capability of actively contributing to frequency regulation in a network and to critically analyze the suitability of having a power plant controller when the PV facility contributes to power-frequency control by means of an inertial response. The results reveal, at the power conversion PV system level, that the inertia emulation module significantly reduces the response times of the installation to changes in frequency -by more than 68% in the worst case under frequency increases, and by 73% under frequency decreases, thus contributing to more rapid power-frequency regulation in the network. However, at the power plant level, the power plant controller disturbs the PV power conversion system's behavior and causes the opposite effect: response times increase -up to more than 7% in the most unfavorable case when the inertia module is enabled. [ABSTRACT FROM AUTHOR]

• A stochastic coordinated generation and transmission expansion planning model is proposed. • The planning model takes into account the impact of demand response programs and renewable energy development. • The detailed demand response model with operating characteristics such as power, energy, reserve, response period, and response number is established. • The chance-constrained method is introduced into the planning model to flexibly control the participation level of DR programs. The integration of high-penetration renewable energy has led to a significant increase in the uncertainties of power systems, and coordinated generation and transmission expansion planning has attracted more and more attention. Demand response resources are regarded as a kind of virtual power supply, which can improve the flexibility of power systems and delay generation and transmission investment to a certain extent. This paper establishes a stochastic coordinated generation and transmission expansion planning model taking into account the impact of demand response programs. In particular, the demand response model describes the operating characteristics such as power, energy, reserve, response period, and response number in detail. Considering the difference in the degree of invocation of demand response programs by different power systems, the chance-constrained method is adopted, which can make the planning schemes flexibly adapt to different demand response levels and improve the practicability of the model. In the case study, numerical results of construction and operation indicate that demand response programs can play a role in replacing generation and transmission capacity. Specifically, although the response energy does not exceed 0.3% of the total load energy, the investment cost can be saved up to more than 20%. Moreover, the response level, flexibility parameters, response capacity, and the proportion of renewable energy will all affect the capacity substitution effect of demand response, which verifies the rationality and necessity of considering these factors in this paper. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of distributed energy management in multi-area integrated energy systems (MA-IES) using a multi-agent deep reinforcement learning approach. The MA-IES consists of interconnected electric and thermal networks, incorporating renewable energy sources and heat conversion systems. The objective is to optimize the operation of the system while minimizing operational costs and maximizing renewable energy utilization. We propose a distributed energy management strategy that makes hierarchical decisions on intra-area heat energy and inter-area electric energy. The strategy is based on a multi-agent deep reinforcement learning framework, where each agent represents a component or unit in the MA-IES. We formulate the problem as a Markov Decision Process and employ Q-learning with experience replay and double networks to train the agents. The proposed strategy is evaluated using a simulation of a four-area MA-IES. The results demonstrate significant improvements in energy management compared to traditional methods, with higher renewable energy utilization and lower operational costs. Specifically, the strategy achieves 100% utilization of wind power, and decreases operational costs by 5.53%. Furthermore, it leverages the generalization capabilities of reinforcement learning to respond in real-time to uncertainties in demand and wind power output. The results highlight the advantages of the proposed strategy, making it a promising solution for optimizing the operation of multi-area integrated energy systems. • Hierarchical framework prioritizes heat-electric energy balance, facilitates joint optimization, enhances system performance. • MADRL-based approach manages dynamic loads, uncertainties, renewables in distributed energy systems. • Proposed method optimizes energy sources, demand, grid constraints in multi-area integration. [ABSTRACT FROM AUTHOR]

• Description of a multi-objective problem that connects two energy and water systems. • Proposing an enviro-economic model to minimize water extraction and operating cost. • Integrating the electrical, thermal, water, and hydrogen energy systems. • Proposing a day-ahead decision-making for optimal management of networked EHs. • The profit of cooperation is divided among EHs based on their contributions. This paper proposes a cooperative multiple-objective approach for the sustainable economic scheduling of networked energy systems to simultaneously optimize the cost of energy and groundwater extraction from underground sources. The suggested strategy enables networked energy hubs to exchange electricity for greater financial gain. Various energy hubs collaborate to maximize gain that is fairly allocated among the hubs to guarantee the coalition's economic stability, according to the collaborative game theory. The coalition achieves its best gain through collaboration and payoff, which is equally and regularly divided among the hubs, according to the Shapley value. The suggested model takes into account a networked system with hubs connected to various types of energy, including electrical and natural gas, renewable energy sources, electrical, and thermal energy storages, combined heat and power units, and gas boilers, cooling, hydrogen, and water systems to meet demands. The numerical results show that the suggested cooperative approach reduces the cost function of the system by 12.71%. The self-discharging rate of battery storage devices can increase the cost function by $ 7.65. Also, the sensitivity analysis on the battery storage sizes demonstrated that they can reduce the total cost of the system by $ 15.65. [ABSTRACT FROM AUTHOR]

• Power sources operation for wind-solar-thermal power system with nonconvex models. • Combination of the federated reinforcement learning (FRL) with the model-based method. • Grid-connected renewable energy and thermal power at a bus are aggregated as a VPP. • Private operation with limited but effective information exchange. • Customized FRL algorithm with efficient agent training and privacy preservation. To schedule power sources operated by different entities in a short-time scale considering nonconvex generation cost and deep peak regulation (DPR) service constraints, this paper proposes an FRL-based multiple power sources coordination framework in wind-solar-thermal power network. In the studied power transmission network (TN), renewable energy sources and thermal power units connected to the same bus are aggregated as a wind-solar-thermal virtual power plant (WSTVPP). The transmission system operator (TSO) sends dispatch instructions to each WSTVPP by optimal power flow program, and allocates the cost of DPR service in TN. Based on the dispatch instruction, the internal power sources of each WSTVPP are scheduled by its local center control agent to achieve local economic operation while maximizing the overall DPR service revenue for the WSTVPP from the auxiliary service market. The multiple WSTVPPs operation is modeled as a partially observable Markov decision process, and solved by a designed FRL algorithm. The FRL algorithm employs a global neural network (NN) model for coordination, heterogeneous local NN models and data to efficiently train each WSTVPP control agent with individual objectives for handling multiple power sources scheduling in TN while preserving local privacy. Numerical studies validate the effectiveness of the proposed framework for handling the short-time scale power sources operation with nonconvex constraints. [ABSTRACT FROM AUTHOR]

• The state-of-the-art research advances in power system restoration with large renewable penetration are reviewed from different system levels, i.e., transmission system, distribution system, and coordinated transmission and distribution system. • The methods of handling renewable power uncertainty during system restoration are summarized. • The laboratory validations and practical applications of the renewable power system restoration strategies are reviewed. • Promising research directions for renewable system restoration are provided, including uncertainty handling, artificial intelligence techniques, transient stability issues, cyber-physical interlinking, and multi-energy coupling. The catastrophic blackout events and ever-increasing penetration of renewable power generation highlight an advanced restoration strategy to effectively and reliably employ renewable power generation to contribute to renewable power system restoration. This paper reviews the research advances of power system restoration involving large renewable penetration, and the methods of handling renewable power uncertainty during restoration are also summarized. First, the transmission system restoration processes are divided into three sequential stages: a) black-start, b) network reconfiguration and c) load restoration, and the research advances of these three restoration stages involving large renewable generation are presented. Then, the distribution system restoration assisted by multiple flexible resources, such as renewable distributed generators, remotely-controlled switches, energy storage systems, and soft open points, are reviewed with emerging techniques, including micro-grids, multi-agent systems, repair crews, and mobile power sources. Furthermore, with the growing activeness and flexibility of the distribution system, the coordinated transmission and distribution system restoration and their information interactions are also discussed. Finally, for practical applications, the laboratory validations of restoration strategies using realistic power grid data, real-world restoration strategies, decision support systems, and field tests of black-start procedures are introduced to complete this literature review. Effectively deploying renewable power sources can significantly improve the power system restoration efficiency, and their inherent uncertainty should be carefully handled. Moreover, promising research trends are provided to improve the power system restoration with large renewable penetration. [ABSTRACT FROM AUTHOR]

The fluctuation and intermittence of renewable energies are raising concerns about the economical scheduling and the security operation of power system. The shift from traditional fossil energies to renewable energies causes the shortage in rotational inertia and affects the frequency stability of power system. One of the promising solutions is to construct a certain number of energy storage facilities with virtual inertia in suitable places for improving stability, which simulates the characteristics of traditional generators through specific controls over the converters of energy storages. While transient issues about the optimal allocation of energy storages have not been well addressed. From the perspective of system inertia support, the system state-space model is firstly established in this paper. Then the guidance of allocating energy storages optimally is provided via ℋ 2 -norm calculation and projected gradient derivation. Moreover, for the purpose of evaluating system disturbances more precisely, an appropriate method for equivalenting the disturbance strength of nodes is proposed, the advantages of which are later examined in comparative simulations. The contribution of this paper is verified through the time-domain simulations of the New England 39-bus system together with the illustration and comparative analysis of the optimal results. • Construct the guidance of allocating energy storages optimally for inertia support. • Propose a method for equivalenting node disturbance and its transmission mechanism. • The proposed method can improve both transient performance and allocation costs. [ABSTRACT FROM AUTHOR]

The identification of critical nodes is important for safe operation and accident prevention in power grids. With the accelerated development of renewable energies, the uncertainty of renewable energy has brought greater challenges to the node importance assessment of the power system. The electrical spreading probability method is proposed in this paper to identify the critical nodes in the power grid containing renewable energy. First, the uncertainty factors of renewable energy are established through probability density functions, and Monte Carlo simulation and stochastic DC optimum power flow are adopted to effectively deal with the impact of uncertain power output from solar and wind energies. Then, considering the system topology and load loss after cascading failures calculated by Monte Carlo simulation, a method is proposed to calculate the probability of nodes being infected. Finally, according to the Susceptible–Infected model, a method depending on the propagation ability of nodes is proposed to identify the critical nodes in the complex power system. The effectiveness of the proposed ESP method is verified through simulation examples of the modified IEEE39 power system and the modified IEEE118 power system. • An electrical spreading probability method is proposed. • By adopting the ESP method, a more accurate assessment result can be obtained. • More effectively eliminate the uncertainty brought by renewable energy integration. [ABSTRACT FROM AUTHOR]

• A robust data-driven predictive load frequency control (LFC) scheme for system with rich wind generation is proposed. • A data-driven LFC scheme is accomplished through learning recursively from the input–output trajectory by employing matrix zonotopes. • The process uncertainties is well considered in generating a robustly optimal control law, and the robustness to the uncertainties caused by renewables could be guaranteed. • The performance of the proposed LFC scheme is verified through comparison with the model-based predictive control scheme, AI-based controller and traditional PI controller. The uncertainties regarding the generations and key electromechanical parameters due to the high penetration of renewable energy bring significant challenges to frequency control. To address this problem, a robust data-driven predictive load frequency control (LFC) scheme for system with rich wind generation is proposed in this paper. To weaken the effects of the uncertainty of the key electromechanical parameters, the data-driven LFC scheme is accomplished through learning recursively from the input–output trajectory by employing matrix zonotopes, instead of accurate predictive model. Since the process uncertainties is well considered in generating a robustly optimal control law, the robustness to the uncertainties caused by renewables could be guaranteed in proposed methodology at each time step. The performance of the proposed LFC scheme is verified through comparison with the model-based predictive control scheme, AI-based controller and traditional PI controller. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• Anovel analytical approach for the frequency stability constraint is proposed. • A frequency-constraint unit commitment model is established. • Adecomposition-based solution of the proposed model is introduced. • The effectivenessof the proposed modelisvalidatedin a real-life system. Modern power systems are confronted with great challenges of frequency instability due to the conventional generators constantly being replaced by renewable energy source. Therefore, it is necessary to consider the frequency stability constraints in unit commitment processes to obtain safer operation plans. In this paper, an analytical approach for the frequency stability constraint is proposed. This approach is given by estimating the largest disturbance the system can withstand. Then, a novel frequency constrained unit commitment model is proposed by incorporating the proposed approach into a unit commitment model. The diverse governor response characteristics are considered in the proposed frequency constrained unit commitment model, and the essential inertia and reserve level are accurately estimated. The proposed model is formulated as a mixed-integer nonlinear programming problem, and generalized Benders decomposition is adopted to facilitate the solution generation. The convergence of the decomposition-based solution is proved. Case studies on the New England 39-bus system and a regional system are carried out where the performance of the proposed model is compared with two existing frequency constrained unit commitment models. The results show that the proposed model can guarantee frequency security while preserving the economic factors in power systems with high renewable energy penetration. [ABSTRACT FROM AUTHOR]

• A hybrid node-branch OPF model for calculating the SVSM of ITDNs is proposed. • Different reactive power-voltage control modes and uncertainty of RESs are considered. • Two distributed bi-level OPF models for the SVSM interval of ITDNs are established. • The ADMM algorithm is used to obtain the SVSM interval of ITDNs distributedly. • Results on different ITDNs show the accuracy and efficiency of the proposed method. To accurately and efficiently calculate the static voltage stability margin (SVSM) interval of integrated transmission and distribution networks (ITDNs) considering the uncertain fluctuation of renewables, a distributed bi-level optimal power flow (OPF) calculation method based on the alternating direction method of multipliers for the SVSM interval of ITDNs is proposed in this paper. Due to the different structure characteristics of transmission network (TN) and distribution network (DN), a hybrid node-branch OPF method for calculating the SVSM of ITDNs is proposed, which has better convergence than the traditional node OPF method. Different reactive power-voltage control modes of renewable energy stations (RESs) are also considered in the calculation. Considering the uncertain fluctuation of RESs, two distributed bi-level OPF models for calculating the upper and lower bounds of the SVSM interval of ITDNs are established. Based on the alternating direction method of multipliers, the SVSM interval bounds of ITDNs can be obtained by solving the sub-optimization models and transmitting the boundary coupling variables and the load growth parameter between the TN and multiple DNs iteratively until the given convergence criterion is satisfied. Test results on two ITDNs, the modified IEEE 39–33 & 69 bus system and an actual 2285-bus ITDNs, demonstrate the high computation accuracy and efficiency of the proposed distributed bi-level OPF method compared with other calculation methods. [ABSTRACT FROM AUTHOR]

• Power control. • Predictive control. • Renewable energy. • Smart grid and wireless communication. As the smart grid structure interconnects renewable energy sources and operators, it is inevitably influenced by the conversion technology and communication system. Investigating the effects of both aspects jointly is an intricate task due to its multifaceted characteristic. In this paper, it is proposed a wireless coded predictive direct power control for a general renewable energy system connected to the grid for smart grid environment. The predictive direct power control processes the received power references that are sent through the wireless channel, and selects the optimal voltage vector to be supplied by the three-phase inverter using the power errors. The wireless communication system, which connects the smart grid operator to the energy systems, is based on Low Density Parity Check and Orthogonal Frequency Division Multiplexing, and in this work, it was used, as a reference of performance, a system that employs Convolutional Coding. The behavior of the proposed power control is evaluated experimentally presenting an operational condition with satisfactory performance. [ABSTRACT FROM AUTHOR]

• System uncertainty is mitigated when promoting the renewable energy consumption. • Various active management elements are integrated in the optimal power flow (OPF). • A more precise and efficient SOCP model for the OPF is presented. • Decentralized P / Q (V) control strategies are proposed for real-time regulation. This paper presents a multi-stage management scheme for renewable-rich medium voltage distribution network (MVDN) to promote the renewable energy consumption and mitigate the system uncertainty. In the first stage, a day-ahead (DA) dispatch model is proposed to promote the renewable distributed generation (RDG) consumption and decrease the power loss. In the second stage, a model predictive control (MPC) based rolling optimization model for intra-day (ID) operation is proposed to minimize the mismatch of the interactive power at the upstream Grid Supply Point (GSP) between the DA schedule and ID operation. Multiple active management elements such as network reconfiguration and soft open point are integrated and a novel second order cone programming (SOCP) model for the centralized optimal power flow in the DA and ID stages is presented. In the third stage, a decentralized P / Q (V) control strategy of RDG inverter for real-time (RT) voltage regulation is proposed to ensure system safety and mitigate the fast voltage fluctuation. The effectiveness of proposed management scheme is demonstrated in a test system integrating a standard network and real-world data of load and RDG output profiles. [ABSTRACT FROM AUTHOR]

Highlights • A model of district heating network is established in detail. • Utilization of the energy storage characteristics of DHN to reduce wind curtailment. • We establish a thermal and electrical coordination stochastic optimal dispatch model. • We use Benders decomposition algorithm to solve the model. • The energy storage characteristics of the DHN is quantitatively analyzed. Abstract The strong coupling between heating supply and power generation in heat and power (CHP) units limits their consumption of wind power and lacks the necessary flexibility. Based on the theory of heat transfer, this paper considers the temperature loss and energy storage characteristics on the transmission side to establish a stochastic optimal dispatch model of considering thermal and electrical coordination. Utilization of the energy storage characteristics of the district heating network (DHN) to reduce the wind power curtailment and accommodate variable wind power. Benders decomposition algorithm is used to solve the model and improve the efficiency of the solution. Finally, the effectiveness of the proposed model and algorithm was verified by a thermal/electrical system example, and the energy storage characteristics of the DHN were quantitatively analyzed. [ABSTRACT FROM AUTHOR]

Highlights • A work package of energy storage system for grid frequency regulation is proposed. • The package includes grid network modeling, ESS sizing, and control algorithms. • The proposal shows ESS is able to maintain frequency under photovoltaic systems. • The required cyclical operation of ESS for frequency regulation remains a concern. • The modularity design of the test system allows various permutations to be tested. Abstract Grid frequency deviation occurs due to the power mismatch and is corrected by a series of control actions carried out by the generators. At present, the correction of the frequency deviation is limited by the ramp rates of generators, which depend on the inertia of their rotating masses. Under the increasing penetration of intermittent renewable energy sources, the task of frequency regulation becomes more demanding in terms of response times due to the frequent power mismatches and reduction of system inertia. An energy storage system (ESS) can be an effective means of regulating the frequency due to its general fast response characteristics. A comprehensive work package is developed in MATLAB/Simulink and Matpower to study how ESS can handle the rapid changes of frequency continuously within a specified window of its state of charge (SOC). This paper includes an IEEE 24-Bus grid network modeling, the sizing of ESS and also proposes ESS controllers with offset control algorithms as the sustainable solution for frequency regulation. As such, the modularity design of the test system allows various types of ESS or loads to be tested. The locations for ESS to be placed on the network are also identified for the best frequency regulation response without causing any grid voltage violations. Meanwhile, the scenarios of high penetration of photovoltaic systems and undersized ESS are also investigated. The results indicate that ESS is able to maintain the grid frequency under the intermittency of photovoltaic systems continuously. [ABSTRACT FROM AUTHOR]

Rural electrification requires high initial capital investments per capita due to its low energy demand and population density. These factors result in a higher cost of electricity than that for urban consumers. Although the solution to the majority of rural electrification financial challenges are with government policymakers, it is equally important for rural electrification project implementers to understand the technical challenges and identify any cost reduction potential. This paper proposes and compares a diverse set of standalone electrification strategies for a variety of consumer load types in the Sdakeni rural area in the Eastern Cape Province in South Africa. The aim of this research is to compare the electrification strategies based on: cost, efficiency, performance, equipment utilization factor, excess electricity produced etc. Based on the findings from the comparison, the paper will provide insight into suitability of such strategies and act as a guideline for balancing cost optimization process and design robustness of such systems. It will also provide recommendations on future research along this line which would include aspects that could not be covered in this work. [ABSTRACT FROM AUTHOR]

• An active phase adjustment control is proposed in this paper to improve the reliability of conventional distance relay in converter-interfaced renewable energy systems. Compared with existing methods, the proposed method does not require any modification of well-established distance relays, which can be directly applied to the interfaced converters of renewable sources. • The proposed method can ensure the distance relay precisely measures fault reactance with the fault resistance up to 100Ω, and the maximum error rate is less than 5% under internal faults. • The proposed method is still effective under the grid codes with the requirements of reactive power generation and negative-sequence current injection. For the distance protection applied in converter-interfaced renewable energy systems, various AC fault ride-through strategies of converter-interfaced sources will cause a large phase difference between the operating current of the distance relay and the fault current, resulting in the mal-operation of the distance relay. To overcome this issue, this paper proposes an active phase control for the converter-interfaced renewable energy systems to enhance the reliability of the distance relay. This control scheme firstly calculates the phase difference between operating current and fault current by only using local measurements. Then, the phase difference is eliminated via phase angle adjustment. To verify the effectiveness of the proposed control scheme. The performance of the proposed method is tested in a radial three-terminal system and the IEEE 14-bus system in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

Highlights • Some Grid Codes impose restrictions on the ramp rate of PV plants. • Hybrid energy storage systems can be more cost efficient for ramp-rate control. • Hybridizing batteries with ultracapacitors expands their lifetime. • The proposed methodology can be used to size the components of the storage. • The total Net Present Cost of controlling ramp rate in a PV plant is optimized. Abstract This paper proposes a methodology for optimal sizing of a Hybrid (battery and ultracapacitors) Energy Storage system for ramp-rate control in PV plants. Frequency stability events can appear in power systems high non-dispatchable renewable energy generation due to sharp power output fluctuations. This has caused TSOs in several countries to require PV and wind plants to implement some method for limiting the pace at which the power injection to the grid is allowed to vary (ramp-rate control). Due to its quick response, properly controlled energy storage consisting of batteries and ultracapacitors can perform this task by dampening quick increases/decreases in the power injection to the grid by absorbing/injecting power at the point of common coupling. In this work, a techno-economic model is presented that can be used to calculate the Net Present Cost over the project life of different energy storage devices used for ramp-rate control by simulating their operation. A real case is studied for a PV power plant is presented. Results are reported comparing different configurations of the energy storage system. [ABSTRACT FROM AUTHOR]

Highlights • A MILP-based multi-objective distribution network expansion planning is proposed. • Importance of several time-blocks is demonstrated for the DNEP formulation. • The proposed DNEP results are analysed based on different costs of unserved energy. • Maximizing DG's utilization is dealt with as well as classical objective function. • Impacts of different penetration limits are investigated on DG's curtailment. Abstract In this paper, the integration of intermittent distributed generation (wind and solar) is dealt with by proposing a multi-objective multistage distribution network expansion planning formulation based on ε-constraint method. This approach allows the planners to obtain pareto-optimal solutions based on the two objective functions: (1) installation costs of distributed generation, substations, feeders, maintenance costs of assets, costs of energy purchased from substations, cost of unserved energy and (2) utilization of renewable distributed generation. To represent the intermittent nature of load and renewable distributed generation, several time blocks are utilized, and its effectiveness is compared with an existing approach. The overall problem is formulated as a mixed-integer linear programming and a linear equation-based substation price is utilized within the optimization framework to reduce the size of the problem. The proposed method is applied to the modified 24-nodes test network for different cases to analyse the utilization of renewable distributed generation from different point of views and it is inferred from the results that the proposed model yields a variety of planning solutions which are better to increase the utilization of renewable distributed generation. [ABSTRACT FROM AUTHOR]

Reliance on renewable energy sources (RESs) such as solar and wind has increased to build a sustainable environment, however, their substantial implementation is hindered by their intermittency. Electric Spring (ES) is one of the technologies to mitigate the intermittent nature of the RESs. In an isolated RES powered microgrid, the ES in conjunction with the non-critical loads in a system like water heaters, refrigerators, and air-conditioners can regulate voltage of critical loads like security system, servers etc. This paper establishes the operating principles of the ES (with reactive power compensation only) and its interaction with RESs based on the understanding of AC power transfer between two sources. The accurate phasors in a system under two scenarios, with and without ES, are drawn. Also, performance of the ES is analyzed and evaluated with respect to variations in the loads (linear) and their types. It is augmented with analytical justifications and validated through simulations and experimental studies. Also, through analytical expressions, simulations, and experiments the importance of the non-critical load on the performance of the ES is illustrated. It is also highlighted that the compensation capabilities of the ES remain the same irrespective of the types of non-critical load. [ABSTRACT FROM AUTHOR]

This paper proposes a simple, passive, voltage-balancing scheme for induction generators directly interfaced with single-phase utility grids. The proposed scheme employs two capacitors for balancing the stator voltage and is insensitive to the machine’s slip. The paper presents the mathematical analysis and an algorithm for the calculation of the two capacitances. Further, the effectiveness of the proposed scheme is demonstrated through the simulation studies in the PSCAD/EMTDC software environment. [ABSTRACT FROM AUTHOR]

The transient stability of power systems is highly affected by the changes in the power generation and load levels. Due to the variability of the generating resources, the system stability can be provoked and the system stability limits can be reached as a result of certain sudden drop in the power generation. In these cases, the load shedding can act as an effective emergency corrective action for keeping system stability; however, over-shedding of loads results in severe economical as well as social security problems. Therefore, minimization of the load shedding required for the restoration of the system stability is one of the main objectives of this paper. Another critical issue related to successful load shedding is the fast assessment of the system stability, and the amount of the load shedding as well as the implementation of the load shedding corrective action. Therefore, this paper presents a fast method of stability assessment and load shedding requirements in the weakly interconnected power system. The method is an improved form of the extended equal area criterion (EEAC) where the required system equivalence is based on the availability of wide area monitoring (WAM) devices in modern power systems. The paper also investigates the impact of the implementation duration of the required load shedding. The results are verified through time domain simulations which confirm the accuracy of the presented method and its suitability for real-time applications. [ABSTRACT FROM AUTHOR]

• An improved day-ahead interval optimization model for power system considering uncertainty of renewable energy is established. • The IADKDE is proposed to obtain a more accurate interval range of uncertain variables to improve the IO scheduling model. • The model considers the economic, EVs losses, pollutant emissions and driving requirements of EVs owners. • The economy and Accuracy of the improved model are verified via case studies and different methods. The increasing penetration of renewable energy causes high uncertainty, which further complicates the optimal scheduling operation of power systems. The uncertainty of renewable energy output is represented by empirical prediction intervals in the traditional interval optimization scheduling model, but the interval range is not precise enough. To address this problem, a novel improved interval optimization method is proposed in this paper. First, the improved adaptive diffusion kernel density estimation (IADKDE) is used to obtain more accurate intervals for the renewable energy output. Furthermore, a data-driven adaptive optimal bandwidth selection is adopted to select the optimal bandwidth instead of normal reference rules in IADKDE. In addition, the day-ahead scheduling optimization model is developed by IADKDE considering the driving requirements of electric vehicles (EVs) owners. The proposed model is described in details and solved by interval linear programming method. Finally, the effectiveness and accuracy of the proposed method are validated, and the comparative analysis with interval optimization by empirical prediction intervals, extreme learning machine(ELM) and stochastic optimization is given. It is demonstrated that the proposed method can obtain more accurate interval ranges of uncertain variables and has strong applicability. [ABSTRACT FROM AUTHOR]

• A peer-to-peer transactive energy trading framework is proposed. • A vertex scenario-based robust optimization is adopted to handle uncertainties. • Taguchi's orthogonal array testing is utilized to reduce the scenario number. • Various network constraints are incorporated to ensure reliable and safe operation. The growing integration of distributed energy resources into the smart grid promotes the transition from passive into active distribution networks. However, this process also brings significant challenges to the system's operation. This paper investigates the peer-to-peer transactive energy trading framework among prosumers in the active distribution network. To handle uncertainties of renewable energy, this paper proposes a vertex scenario-based robust optimization method with Taguchi's orthogonal array testing method, which can effectively reduce the scenario number and improve computational efficiency. The developed model aims to minimize the overall cost of multi-energy resource owners considering the power loss incurred by the transactive energy trading. Various network constraints are incorporated to ensure the reliable and safe operation of the distribution network. The Nash bargaining theory is adopted in the transactive energy trading payment allocations, which can obtain mutually beneficial and fair solutions to encourage prosumers' participation. Finally, several case studies are conducted on a modified 69-bus distribution network to show the effectiveness and superiority of the proposed approach in terms of improving flexibility, economy, calculation speed, and security. [ABSTRACT FROM AUTHOR]

Renewable Energy Sources (RESs) stand as a straightforward alternative to complement the energy grid and have been recurrently integrated into the generation and distribution matrices. Physically, generation components have distributed nature and they are expected to work coordinately, complementing each other to supply centralised microgrids, which interface them with the main power grid and consumers. As RESs have intermittent nature, engineers are limited to plan resources consumption, fulfil unit commitments, stratify time, cost, environmental impact, and quality of energy. This paper exploits the event-driven behaviour of microgrids to provide ways for them to recognise their context and cooperate towards common goals. Our main result is a Petri net simulation model that estimates customisation policies to be applied back over the microgridvia event-based control. Results show that in some cases it is possible to save effort (and resources) of power generation by replacing reactiveness for a more balanced, context-aware, energy chain. A real photovoltaic plant is modelled by our approach with 86% accuracy. • A formal method is developed to control the event-driven behaviour of distributed power system components. • A simulation model is proposed to anticipate cooperation policies for generation plants. • A case study applies the models for resources planning in a real photovoltaic plant. • A literature review is discussed and comparisons are provided wherever possible along the paper. [ABSTRACT FROM AUTHOR]

The planning and design of an electric power system, including high-voltage direct-current transmission, is a complex optimization problem. The optimization must integrate and model the engineering requirements and limitations of the generation, while simultaneously balancing the system electric load at all times. The problem is made more difficult with the introduction of variable generators, such as wind and solar photovoltaics. In the present paper, we introduce two comprehensive linear programming techniques to solve these problems. Linear programming is intentionally chosen to keep the problems tractable in terms of time and computational resources. The first is an optimization that minimizes the deviation from the electric load requirements. The procedure includes variable generators, conventional generators, transmission, and storage, along with their most salient engineering requirements. In addition, the optimization includes some basic electric power system requirements. The second optimization is one that minimizes the overall system costs per annum while taking into consideration all the aspects of the first optimization. We discuss the benefits and disadvantages of the proposed approaches. We show that the cost optimization, although computationally more expensive, is superior in terms of optimizing a real-world electric power system. The present paper shows that linear programming techniques can represent an electrical power system from a high-level without undue complication brought on by moving to mixed integer or nonlinear programming. In addition, the optimizations can be implemented in the future in planning tools. [ABSTRACT FROM AUTHOR]

The increasing integration of Electric vehicles (EVs) and renewable generators (RGs) is an inevitable trend of power grid concerning the global control of greenhouse gas release. This will challenge constrained distribution networks due to the intrinsic intermittency of renewable power and additional charging load demand of EVs. However, appropriate dispatch of EVs via vehicle-to-grid (V2G) operation in coordination with RGs can solve the stability issues, provide operational support for the power grid, reduce the reliance on fossil fuels and benefit both the environment and EV users. This paper develops a novel algorithm to solve the optimal decentralized coordinated dispatch problem of RGs and EVs based on multi-agent and the A ∗ search procedure. This optimal dispatch problem is formulated as a distributed multi-objective constraint optimisation problem using the Analytic Hierarchy Process taking into account both power grid and EV users’ concerns and priorities. The inherent uncertainty of EV driving activities and RG output power are considered in this work, of which the stochastic modelling is established using copulas. The proposed algorithm is tested on a radial distribution network, a modified UK generic distribution system. The simulation results demonstrate the feasibility and stability of the proposed A ∗ -based optimal coordinated dispatch strategy. [ABSTRACT FROM AUTHOR]

• Power quality management for 12-pulse rectifier is established to improve its voltage and current adaptability. • Power quality of distribution systems is managed based on the magnetic potential balance. • An energy-based control scheme is proposed to achieve the optimal power quality. • Lower dc-link voltage and better power quality can be achieved. This paper proposes an energy-based control scheme for optimal power quality management in 12-pulse rectifier integrated distribution systems. In this 12-pulse rectifier integrated distribution system, instead of using a step-down transformer for voltage matching, a back-to-back converter is connected to the taps of star and delta windings on 12-pulse rectifier transformer's secondary side via the coupling LC. Using the magnetic potential balance of harmonics and reactive components between the back-to-back converter and the load side of the 12-pulse rectifier, the power quality of distribution systems can be managed. This topology can improve the voltage and current adaptability of the converter, thereby saving the cost of voltage matching transformer and exploiting the filter converter capacity potential. To ensure global power quality performance, an energy-based control scheme is proposed to improve the power quality management performance by accelerating the energy dissipation of the system. Case studies are performed to verify the effectiveness and superiority of the proposed methodology on low dc-link voltage and power quality. [ABSTRACT FROM AUTHOR]

Vehicle to Grid (V2G) technology provides an economical, fast response and highly flexible capabilities for grid frequency regulation (FR). However, denial of service (DoS) attacks disrupt the communication links between V2G FR controllers and electrical vehicle (EV) aggregators leading to severe degradation of FR performance. Therefore, this paper proposes an attack intensity-aware adaptive critic design (AIACD) to improve FR performance under DoS attacks. In the proposed AIACD, a game framework is constructed to derive the optimal robust V2G FR controller, considering attack intensity and V2G power limits. The optimal V2G FR controller is approximated by neural networks to minimize a proposed attack intensity-aware cost function. The cost function is modeled as a function of both system states and attack intensity so that minimizing the cost function to optimize the V2G FR controller for the corresponding attack intensity. The IEEE 39-bus cases with three areas illustrate the effectiveness and advantages of the proposed AIACD under DoS attacks. • A DoS attack intensity-aware adaptive critic design based FR controller is proposed. • A zero-sum game framework is constructed to derive the optimal robust controller. • A cost function is constructed to map attack intensity to FR performance. • An RL is constructed to exploit the attack information to optimize the controller. [ABSTRACT FROM AUTHOR]

• Planning battery energy storage systems (BESS) under weak grid condition requires a thorough analysis; The location and sizing of the BESS was modelled as a constraint optimization problem. • The optimization problem was solved using a heuristic approach called Binary Grey Wolf Optimization. • Simulation results showed improvements in voltage and frequency stability when compared with existing works. Parallel to the fast uptake of renewable energy sources (RESs) connected to the grid, the electric power industry has experienced a number of issues related to system strength and inertia. Battery energy storage systems (BESSs) have been proved effective in mitigating numerous stability problems related to the high penetration of renewable energy sources. This paper investigates the role of BESSs in mitigating the voltage and frequency stability issues in weak grids. We utilize a binary grey wolf optimization approach to define the locations and sizes of BESSs to improve voltage and frequency stability in a weak grid. Simulation results show that compared to existing solutions, significant improvements in terms of voltage and frequency stability can be achieved by implementing our proposed solution. [ABSTRACT FROM AUTHOR]

• Analyzing the steady-state voltage stability problem based on maximum entropy. • Kmeans technology is used for scene clustering to reduce the linearization error. • Loading margin is utilized as an evaluation index due to its clear physical meaning. • The correlation degree of the output influences fluctuation of loading margin. The penetration of renewable generation, such as photovoltaic and wind power in power networks will significantly impact the system's stability in future, due to randomness and intermittency in the natural environment. Moreover, the correlations between renewable generation may enhance the impact on power system's stability. In this paper, cumulant-based maximum entropy method (CMEM) combined with Nataf Transform (NT) is proposed to analyze the steady-state voltage stability problem considering correlations and uncertainties of power injections and consumptions. The proposed methodology is tested on the IEEE 30-node and IEEE 57-node test systems. Taking the results of Monte Carlo method (MCM) as the experimental control group, this paper compares the proposed method with series expansion method (SEM), discusses and analyzes their calculation results and speed under different scenarios, and the results of the comparison prove the validity, accuracy and rapidity of the CMEM. [ABSTRACT FROM AUTHOR]

Abstract: Recently, the deregulation and liberalization in power market increase the competition in retail and power sector. Therefore, power company needs to reduce operational cost and maximizes the profit by operating generator with higher efficiency. On the other hand, energy consumption is increasing rapidly due to the proliferation of all-electric houses. So the controllable load such as electric water heater, heat pump and electric vehicles are introduced to the power system. However conventional system does not operate the thermal unit and controllable load in a coordinated manner. This paper focuses on operating the controllable load, battery and thermal units in a coordinated manner. Furthermore, this paper considers the transmission constraints. The proposed method uses a tabu search for optimization method. Optimizing procedure is divided into two parts. Firstly, actual load is controlled by controllable load and battery. Finally, the schedule of thermal unit commitment problem is decided in the revised load. Simulation results show the validation of proposed method and validate the performance and effectiveness of the algorithm for controllable load and battery. [Copyright &y& Elsevier]

As numerous generation side assets, such as the generating units and the plant equipments, in the Smart Grid system have already aged, well planned maintenance and operational scheduling is needed to maintain their expected longevity. Forced outages and short-notice/temporary maintenance of thermal units should also be considered as likely events. Backup energy sources are required to replace the generating units during the maintenance period. Using conventional storage devices for this purpose is feasible, but costly. Electric vehicles and plug-in hybrid electric vehicles, with vehicle-to-grid capability, referred to as “gridable vehicles”, are a useful choice as storage devices for the same. Second life batteries, disassembled from gridable vehicles after passing their automotive life, are another candidate for this purpose from the economic perspective. A system model that aggregates gridable vehicles and second life batteries together in an intelligent way to provide such backup energy can reduce significant storage costs. Such a system model is presented in this paper for a Smart Grid environment. Our simulation results suggest that using gridable vehicles and second life batteries together can save up to 70% of conventional storage energy costs and recover capital costs for the second life batteries in only 1.5 years. [ABSTRACT FROM AUTHOR]

• A novel metric called similarity is proposed to further enrich the traditional TPI theory. • A TPI-valued LOLE metric is constructed to avoids the shortcomings of traditional point value reliability metric. • We propose a TPI-valued UGF algorithm, which can give the most likely value of the reliability evaluation result and its fluctuation range simultaneously for power systems. With the increasing integration of renewable energy in the power system, its strong volatility and randomness will greatly affect the planning and operation of the power system. In this situation, compared to the traditional point-value-based reliability assessment results, system operators prefer the results which can give the most likely recommended values and their possible ranges. To this end, in this paper, we present a novel framework to evaluate the reliability of multi-state power systems that incorporates three-parameter interval number theory and improved universal generating function method. In particular, we first improve the three-parameter interval number theory by proposing a similarity-based indicator to describe the distance (or closeness-degree) between two three-parameter interval numbers and redefine the probability that one three-parameter interval number is greater or less than another one. Secondly, we extended the traditional universal generating function to a three-parameter interval number-based universal generating function. Moreover, the three-parameter interval number-based universal generating functions of conventional generators, renewable energy generators and load demand are further established to describe their multi-state characteristics. At last, a three-parameter interval number-based loss of load expectation metric is presented for reliability evaluation. Compared with the existing methods, the proposed framework can well evaluate the reliability of multi-state power systems, and can describe the uncertainty of reliability evaluation results through the three-parameter interval number, thus providing the most likely value of the evaluation result and its fluctuation range at the same time. In the numerical simulation part, a comparison of five different scenarios is performed on an IEEE Reliability Test System. The results show that the proposed method is suitable for the reliability assessment of power systems with high penetration of renewable energy, which can describe the fluctuation range and indicate the most possible value of reliability simultaneously. [ABSTRACT FROM AUTHOR]

• A quantitative evaluation index of the complementary characteristics of renewable energies is introduced. • Multi-objective model for optimal capacity planning of DES with renewable energies is established. • A hierarchical optimization framework is presented to implement the multi-objective optimization. • Case studies considering the CROF and total economic cost objectives are conducted. Distributed energy system (DES) integrated with renewable energy (RE) has attracted extensive attentions and developed rapidly for its high energy utilization rate and sustainability. Both solar and wind, as typical renewable energy, are usually considered in researches on DES while the complementary fluctuation characteristic of solar and wind is seldom taken into optimization objective during the planning process. This paper introduces a quantitative evaluation index of the complementary characteristics of renewable energies and proposes a multi-objective capacity planning method for DES to minimize the economic cost and to maximize the complementarity rate of fluctuation (CROF) simultaneously. To solve the established multi-objective model, a hierarchical optimization framework is employed, which remodels the multi-objective model into multi-layer optimization model by reconstructing new constraints. The nonlinear part of optimization model is linearized by linearization technique. Finally, branch and bound algorithm is employed to obtain the solution to optimization model. Finally, case studies based on typical daily load characteristic curves in three kinds of seasons were performed and analyzed. The results verify the feasibility and effectiveness of proposed method, indicate that the complementarity rate of fluctuation can be improved by 53%, and the average relative fluctuation rate of power purchased from grid can be reduced by 58.7% compared with single economic objective model. [ABSTRACT FROM AUTHOR]

• A framework is proposed for managing the supply–demand balance after the failure of components of the distribution grid. • The interactions between the operator and agents in the post-contingency condition are modeled by the Stackelberg game. • The strong duality concept is taken into account to determine the optimal solution of the Stackelberg game. • An algorithm is proposed to implement the optimization considering the isolated and grid-connected areas of the network. Emergence of reformation and privatization in energy systems has caused the development of multi-agent distribution systems. In this context, each agent as an independent entity aims to efficiently operate its respective resources; while, the distribution network operator (DNO) strives to control the grid in an efficient and reliable manner. Respectively, in case of failure incidences in the grid, DNO should address the economic losses as well as reliability concerns of the agents. Consequently, this paper intends to organize a framework that enables the DNO in order to incentivize the cooperation of agents to alleviate operational effects of the contingency condition. Accordingly, DNO provides bonuses to agents to modify their scheduling with the aim of optimizing the incurred operating costs in post-contingency conditions. Respectively, Stackelberg game is applied to model the incentivizing resource scheduling optimization in post-contingency conditions, and strong duality condition is used to re-cast the preliminary bi-level model into a one-level mathematical problem. Furthermore, a step-wise strategy is illustrated to facilitate the application of the obtained optimization model while considering islanded areas in the grid. Eventually, the proposed strategy is implemented on the IEEE-33-bus-test-network to examine its usefulness and applicability in management of the distribution networks in post-contingency conditions. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines the integration of new sources of renewable energy into the power systems in Europe—challenges and possible solutions, application of wind power prediction tools for power system operation, new tasks that create new solutions for communication in distribution systems, wind power in Greece, integration of dispersed generation in Denmark, wind power in The Netherlands, EdF and distributed energy resources in France, and new renewable sources in Italy. The paper also examines the European Commission Technology Platform’s vision paper on Electricity Networks of the Future that was published in January 2006. In this respect, drivers towards Smart Grids, Grids today, and key challenges for Smart Grids of the Future are critically assessed. [Copyright &y& Elsevier]

• Conception of desired range is presented to compress sampling space. • Importance sampling conducted in the desired range improves sampling efficiency. • Uniform sampling and high calculation accuracy are achieved by Hammersley sequence. • Eigen-decomposition method has wider applicability to solve correlation problems. This paper presents a novel probabilistic power flow calculation method for power systems with integrated wind farms, based on importance sampling and Hammersley sequence with eigen-decomposition. The method proposed in this paper adopts importance sampling to build probability density functions in a desired range to compress the sampling space and hence reduce the calculation burden. The Hammersley sequence, a kind of low discrepancy sequence, is used to obtain uniform samples for improving the sampling efficiency. In addition, since the traditional Cholesky method is unable to decompose the non-positive definite correlation matrix, this paper applies the eigen-decomposition to solve this problem for multiple correlated wind sources. Case studies are conducted on modified IEEE test systems, where the advantages of the proposed method are verified. According to the simulation results, the proposed method shows greater accuracy and efficiency, compared to the traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, the analysis of phosphoric acid fuel cell (PAFC) system is presented. Various control schemes of power management e.g. current, voltage, power, reactants flow pressure and temperature based control schemes are proposed with simple and easy to implement PI controller for PAFC operation. These control schemes can be utilized for power management purpose. The proposed model of PAFC along with these control schemes is realized in the MATLAB/Simulink environment. The performance of a PAFC system along with these proposed control schemes is found to be satisfactory even under dynamic conditions. [ABSTRACT FROM AUTHOR]

• Definition of an algorithm that maximizes the social welfare for all participants in the system. • Definition of an incentive mechanism that is designed so that a true declaration of preferences and participation in the mechanism itself is the optimal strategy for each electric vehicle owner. • Maximization of the renewable energy sources utilization for the electric vehicles charging. With an increasing number of electric vehicles, the active involvement of the electric vehicle owners in the electricity market, as well as indirect participation in the power system control, seems to be the only option for stable operation of the power system. However, the electric vehicle owners do not know or care about the operation of the power system and follow just their own interests. Our paper defines an algorithm that controls the charging of electric vehicles within a small grid-connected microgrid with renewable energy source. The power produced from the renewable energy source is utilized to charge electric vehicles, and so maximization of social welfare is secured. The control algorithm solves an optimal control problem with the model predictive control approach taking into account expectations of the development of the future preferences. To motivate electric vehicle owners to participate in such a system, the incentive mechanism is defined so that the truthtelling is an optimal strategy for each participant of the system. Results from the simulation confirmed the stated assumption that the algorithm maximizes utilities experienced by the electric vehicle owners and that truthtelling is the optimal strategy for each participant in the system. Nevertheless, the results showed that the demand for charging followed power produced by the photovoltaic power plant, when possible, so the charging station's influence on the power system was neglected. [ABSTRACT FROM AUTHOR]

In solar photovoltaic (PV) power generation systems, availability impacts directly on annual energy production capacity. In order to reveal availability levels, the system is usually constructed and monitored. Besides to be expensive and time consuming, this approach fails to provide in-advance estimations that could benefit planning, dimensioning, and predictive maintenance. This paper proposes a Petri Net-based model that can anticipate, within a short period of time and acceptable level of accuracy, availability levels for PV systems with different sizes, architectures, generation profiles, and stages of construction. By receiving a set of offline input parameters, the model can be exploited in advance, before actually constructing the real infrastructure to be measured. Simulations show that the system behavior under variability can be anticipated with reasonable accuracy, which tends to be valuable for predictive engineering. A real PV system model illustrates our approach. [ABSTRACT FROM AUTHOR]

Aiming at the complicated power quality problems with the coexistence of voltage quality and current quality in Microgrid, a comprehensive regulation technology of power quality is urgently needed, which makes unified power quality conditioner (UPQC) have widespread application fields. As a multiple power quality conditioner, UPQC gathers the functions of voltage quality compensator, current quality compensator and energy storage device in the integrated manner, meeting the needs of comprehensive power quality control for the Microgrid. In this paper, the advanced H ∞ control theory is adopted for further development on the UPQC generalized controlled system, and the Riccati solution method of the H ∞ controller is presented. Through analyzing the coupling effect between UPQC series unit and shunt unit, a design method of UPQC coordinated controller based on H ∞ control theory is proposed. The simulation and experimental results show that the proposed method can realize UPQC series unit voltage tracking compensation and shunt unit current tracking compensation simultaneously without steady-state phase shift and amplitude degradation. Moreover, the proposed method radically eliminates the coupling effect between UPQC series unit and shunt unit, and realizes the multiple power quality coordinated control functions of UPQC. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) with vehicle-to-grid capability, referred to as "gridable vehicles" (GVs), have become a useful choice for storage devices in the smart grid environment. The success of a smart grid system with GVs heavily depends on the market penetration of GVs and their participation rate in the vehicle-to-grid (V2G) program. Battery lifetime and revenue earning potential are influential factors for GV owners considering whether to participate in the V2G program. Second use of GV batteries, after their automotive life, can generate some revenue for the owners to recover a part of their battery purchase cost. However, this is dependent on the remaining capacity of the battery and the capacity degradation rate both in the automotive and second life. In this paper, capacity degradation and the remaining energy of a GV battery at different operating cycles have been quantified in both their automotive and second lives. Cost of battery energy both in automotive and second life have also been modelled that informs the owners of the revenue potentials, especially from the second life use. Finally, an economic load dispatch model with the inclusion of second life revenue has been developed to establish that using GV batteries in this way would earn extra revenue thus contributing to the initial buying price and encouraging more GV participation in the smart grid. Simulation results show that the proposed model can contribute up to 19.56% of the initial battery purchase cost, while still ensuring economic load dispatch. [ABSTRACT FROM AUTHOR]

• Investigation of the cascaded impact of time delay on MG stability and impact of stability on power management. • Including the stability criteria in the MG power management optimisation. • Providing the solution to maintain the stability while optimising the MG cost. • Improving the communication time delay to improve stability. • Adding inter-operability to MG control elements to reduce engineering time and communication delay. Power management, system stability and communication structure are three key aspects of microgrids (MGs) that have been explored in many research studies. However, the cascaded effect of communication structure on system stability followed by the impact of stability on the power management has not been fully explored in the literature yet and needs more attention. This paper not only explores this cascaded impact, but also provides a comprehensive platform to optimally consider three layers of MG design and operation from this perspective. For generation cost minimization and stability assessment, the proposed platform uses an adaptive particle swarm optimization (PSO) while a new class of data exchange scheme based on IEC 61850 protocol is proposed to reduce the communication time delays among the inverters of distributed generations and the MG control center. This paper also considers the system stability using small-signal model of a MG in a real-time manner as an embedded function in the PSO. In this context investigations have been conducted by modeling an isolated MG with solar farm, fuel cell generator and micro-turbine in MATLAB Simulink. Detailed simulation results indicate the proposed power and stability management method effectively reduces the MG generation cost through maximizing the utilization of the available renewable generations while considering system stability. [ABSTRACT FROM AUTHOR]

• The evolution, basic principles and characteristics of DL are summarized. • Eight typical DL structures and five popular frameworks are introduced. • The feasibility and adaptability of DL applied to frequency issues is analyzed. • Review and analyze current research of DL in frequency applications. • Systematically discuss effectiveness of DL and potential research directions. The penetration of renewable energy resources (RES) generation and the interconnection of regional power grids in wide area and large scale have led the modern power system to exhibit more and more complex dynamic features, such as time-varying nonlinearity, uncertainty, data diversity, and local observability. The increasing complexity of power system's dynamic characteristics makes the traditional analysis and control methods inefficient, even invalid. As a new technology path of Machine Learning, Deep learning (DL) has distinct advantages in solving complex problems such as power system frequency analysis and control due to its powerful ability of data analysis, prediction, and classification. This paper reviews the history, state of art and the future of the DL's application in power system frequency analysis and control. Firstly, the basic principle and research progress of DL, the training methods, typical structures, and application peculiarity of DL were introduced. Secondly, the application status of DL in frequency situation awareness, frequency security and stability assessment, and frequency regulation and control were summarized, and the adaptability of DL application to each kind of issue was discussed. Finally, the development trend of DL and its application in power system frequency were prospected. [ABSTRACT FROM AUTHOR]

Typically, residential buildings neither allow flexibility in the individual contract power capacity nor considers buildings as unique electricity consumers. In this work, a smart building is designed that each electricity customer has flexible contract power and the whole collective residential building has a single contract power. A management entity is considered to manage all energy resources of the building such as the photovoltaic generation, electric vehicles, and battery energy storage system, taking into consideration the consumption from apartments and common services, to minimize the electricity bill. Hence, the best/optimal contract power capacity will contribute to minimizing electricity costs. Therefore, finding the optimal decision of the contract power value has received a significant role from the energy management in smart buildings. In this paper, a mixed binary optimization problem is formulated in which not only the optimal value of contract power is yield but the optimal schedule of the electric vehicle/battery storage charge and discharge are found, taking into consideration the photovoltaic generation and load consumption profiles. The proposed model is implemented for three scenarios, and the obtained results show that the model efficiency has a high performance with a significant electricity cost reduction, around 47%. The results pointed that using an optimal value of single contract power and intelligent management system, the building electricity costs decrease remarkably. • Building energy resources management with intensive use of electrical vehicles. • Flexible contract power capacity. • Optimal single contract power for multi-unit residential buildings. • Mathematical modeling towards energy efficiency and electricity reduction costs. • Energy storage energy in residential building context. [ABSTRACT FROM AUTHOR]

• Provided a priority-based Demand Side Management (DSM) concept to improve user satisfaction in rural microgrids. • Suggest a simple load prioritization technique to reduce the complexity, during the implementation of DSM. • A priority-based algorithm has been presented to maximize the hours of services for essential loads. • Mathematical modelling of the microgrid system has been developed and tested in a simulation-based environment. Microgrid (MG) is one of the practical and best concepts to provide energy access to rural communities, where electric grid extension is not techno-economically feasible. Since the trend of load consumption is not uniform with a low load factor in a rural area, the required rating of the system becomes very high. Similarly, the generation is fixed for these MGs, whereas the load increases continuously over time. Such a system faces supply deficit issues triggering a high number of interruptions that may cause frequent blackouts. Hence, rolling blackout and load clipping techniques are preferred during the peak load period in most of the rural MGs. These issues lead to an unreliable power supply and low satisfaction level of the user. This paper presents the load prioritization technique to guarantee the continuous supply for the essential loads within the rural community. A day-ahead energy allocation technique is mathematically formulated and optimized to maximize the total hours of energy served. This technique maximized the hours of energy served to the load with higher priority followed by the load with lower priorities. From this study, it is found that the proposed strategy helps to improve the hours of energy served in the overall system, by improving the state of charge (SoC) level of the battery system. The result shows that the user satisfaction level has been improved by 5% through 100% of continuity for the essential loads. [ABSTRACT FROM AUTHOR]

• Model considering uncertainties based on difference regional resources is proposed under quantile regression. • Our model is designed to reproduce the asymptotic distribution and the diurnal variation of power output. • Our model takes uncertainties of renewable energy and loads into account via scenario method. • The economy and superiority of the model are verified via case studies and analyses. This paper proposes a stochastic economic dispatch strategy considering uncertainties of source and load based on difference regional energy resources. A improved autoregressive model is used to fit output and forecast error data of wind, photovoltaic, and load output for reproducing the asymptotic distribution and diurnal variation. The improved model generates a large number of deterministic scenarios to display the uncertainty and intermittency. Considering uncertainties of source and load based on difference regional energy resources, the quantile scenario reduction method is proposed to obtain different scenarios for ensuring the stochasticity of renewable energy and load. The quantile regression principle in statistics is used to set weights for different quantile scenarios. The proposed multi-scenario stochastic dispatching problem is converted into a MILP model for solving. Some conventional generators of the standard IEEE 39-bus system are connected with renewable power sources for study purpose. Actual data of a regional grid of China is analyzed. The simulation results of the model and strategy are summarized, analyzed and compared in this study. The simulation results show that the cost is reduced by 1.78% and 1.692% compared with the deterministic case and the traditional method. The proposed method could meet its different dispatch requirements and provide a theoretical basis for the generation of dispatching plans. [ABSTRACT FROM AUTHOR]

• A FTFOSMC is proposed for performance enhancement of the PMSG wind turbine. • The proof is presented for the stabilization of the PMSG wind turbine under the proposed FTFOSMC. • The upper limited convergence time of the PMSG wind turbine under the FTFOSMC can be estimated. • The simulations are taken to validate the superior performance of the PMSG wind turbine under FTFOSMC. This paper proposes a fixed-time fractional-order sliding mode controller (FTFOSMC) for the permanent magnet synchronous generator (PMSG) wind turbine to enhance the power quality. The FTFOSMC is implemented in the rotor side converter (RSC) and the grid side converter (GSC) to stabilize the PMSG wind turbine within an upper limited convergence time and to improve the dynamic performances. The fractional-order calculus is taken to devise fractional-order controller and the expression of the FTFOSMC is a continuous description, which can better weaken the chattering phenomenon of conventional SMC. Meanwhile, fixed-time stability is adopted to ensure the stabilization of the PMSG wind turbine within an upper limited convergence time irrelated with the initial states. The detailed proof is presented to validate that the proposed FTFOSMC can ensure the PMSG wind turbine stable and estimate the upper limited convergence time through Lyapunov function. Finally, several cases, including peak power tracking and reactive power control, variable wind speeds, grid voltage disturbances and experimental verification, are studied to show the effectiveness, robustness and superiority of the FTFOSMC than the PI control. Simulation and experimental results show that the FTFOSMC can achieve superior dynamic performances than the PI control with shorter convergence time, less vibration, higher tracking accuracy and stronger robustness, thus improving power quality of the PMSG wind turbine. [ABSTRACT FROM AUTHOR]

Highlights: [•] A comprehensive survey on the VSG literature, VSG topologies and achievements are presented. [•] The fundamentals and concept of VSG as a solution towards stability improvement of future grids are addressed. [•] The paper addresses a new VSG-based frequency control scheme. [•] We focused on the poetical of VSGs in the grid frequency regulation task. [•] The main relevant technical challenges, key issues, and further research needs are also emphasized. [ABSTRACT FROM AUTHOR]