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]

• 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]

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]

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]

• 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]

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]

• 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]

• 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]

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]

• 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]

• 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]

• 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]

• 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]

• 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]

• Proposing climate-friendly and socially inclusive AC-DC renewable energy system. • Designing overlay multi-terminal HVDC network for large-scale access to renewable power. • Formulating fully distributed optimal power flow solution for AC-DC systems. • Introducing operational consensus constraints to support data privacy. • Designing corridors for infrastructure combination of HVDC cables and motorways. A large-scale integration of wind and solar energy relies on a grid that allows for the renewable electric power to securely reach the load centers. While the necessity for the extension of transmission capacity to obtain a climate-friendly and sustainable overall energy system is given by physical evidence, the realization of any extension relies on social acceptance and the support of all involved stakeholders. This is where the overall contribution of this paper comprises three dimensions. Firstly, thanks to the modular multi-level converter (MMC), it has become possible to realize multi-terminal DC overlay networks that lend themselves for connection with AC grids. The full AC-DC integration is at the core of the first dimension and aimed at offering a high flexibility of control when addressing security and efficiency. Secondly, as the AC-DC grid may stretch across multiple countries and therefore also diverse transmission system operator (TSO) control areas, it is important that a desired degree of data privacy and autonomy within those areas can be preserved. This is made possible by the proposed distributed AC-DC power flow optimization based on a modified fully parallel Alternating Direction Method of Multipliers (ADMM). The formulated local physical consensus constraints for boundary variables at the locations of tearing only rely on exchange of information with immediate neighbors. The physical consensus constraints are complemented by the newly introduced operational consensus constraints to prevent conflicting operational control actions. Thirdly, it is shown how social acceptance can be boosted by having the transmission lines share the corridors with other transport infrastructures. Of particular interest in this context are DC cable tunnels along motorways and the joint usage of bridges, leading to a combination of infrastructures. This also readily supports the development of electric vehicle charging stations along motorways. Together, these three dimensions provide the foundations of the proposed OVANET concept of socially inclusive AC-DC grids for renewable energy transmission. [ABSTRACT FROM AUTHOR]

• A phasor-based fault location algorithm (FLA) is proposed in a three-terminal transmission line (TL) connected to an industrial microgrid. • The FLA does not require the parameters of the TL. • The FLA does not require the data of faulty phase detection. • The FLA does not require to detect the faulty section in three-terminal TLs. • The FLA is robust to the variations of different parameters of the industrial microgrid and uncertainties of renewables-based DGs. The connection of large industrial microgrids to three-terminal transmission lines greatly complicates the protection scheme of the lines due to the uncertainty in generating renewable energy sources, the probability of unintentional islanding, the abrupt switching of sensitive and critical loads, the influence of supply and demand management programs, and bidirectional active and reactive power flow. This paper presents a fault location algorithm for three-terminal transmission lines connected to an industrial microgrid that does not need the detection of faulty section, fault classification, and transmission line parameters. The proposed method uses voltage and current phasors of transmission lines measured by phasor measurement units (PMUs). One of the salient features of the proposed algorithm is that it does not need to determine the faulty section before the fault location operation. Furthermore, since power transmission lines are always exposed to changing climatic conditions and aging, their electrical characteristics vary over time in the long run compared to their rated value. Hence, this leads to the malfunction of protection schemes. This is solved in this study as the proposed fault location algorithm is independent of line impedances. On the other hand, the dependence of conventional fault location algorithms on fault classification data reduces the reliability of fault location schemes, making these algorithms dependent on fault classification data. However, the present study overcomes this problem as the algorithm is independent of the data of the faulty phase(s) detection. The proposed scheme is fully implemented in the MATLAB software environment, and the performance of the algorithm is analyzed for various fault types under different conditions. The validity of the algorithm is shown by various tests and sensitivity analyses. [ABSTRACT FROM AUTHOR]