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• Classified sales channels for different quality renewable energy. • Dynamic assessment indicators for the renewable energy's power deviation. • Power curve optimization strategy considers the self-owned battery. • Accurately and timely transmit the peak shaving pressure. • Guarantee the independent decision-making power of new energy stations. By the end of 2023, the installed capacity of renewable energy (RE) in China accounted for 36.0% of the total installed capacity, while the installed capacity of conventional thermal power units had decreased from 64.0% in 2016 to 47.6% in 2023. RE accounts for a higher proportion, leading to a shortage of peak shaving resources (PSR). As a result, the current RE quota system becomes unsustainable. In this paper, RE stations can classify their electricity into high-quality and low-quality parts based on the accuracy of their power curves. High-quality RE will be fully purchased and under stricter assessment, while low-quality RE will compete for clearing in the intraday spot market. Firstly, the article estimates the degree of PSR shortages in different periods based on technical parameters of thermal units, RE forecast information, and load forecast information. Consequently, the PSR supply capacity (PSR supply capacity) can be divided into PSR shortage status and sufficient status. Secondly, a clearing method for low-quality RE in the spot market is designed. The grid can set different PSR supply capacitys for different time periods according to the degree of PSR shortage. The assessment indicator values vary under different PSR supply capacitys. Lastly, based on the classification sales channels, this paper proposes a power curve optimization strategy that considers self-owned battery energy storage (BES). Case studies show that the proposed classification sales channels can effectively decrease the degree of PSR shortage while taking into account the benefits of RE station. The designed PSR supply capacity dividing method can accurately reflect the degree of PSR shortage in real-time, and the proposed power curve optimization strategy can further enhance the economic benefits of RE station. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• 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]

• A multi-microgrid energy real-time optimal control scheduling strategy is proposed. • Energy storage devices can actively participate in optimal energy scheduling. • Improved resilience and flexibility of energy dispatch for multiple microgrid. • Significantly reduce the number of microgrid connections to the distribution grid. • Maximising the full uptake of renewable energy in multi-microgrid systems. With the increasing prominence of the energy crisis and global climate issues, it has become an important cornerstone of the energy revolution to improve the utilization rate of renewable energy and gradually reduce the use of fossil energy. Nonetheless, the erratic fluctuations and instability of renewable energy sources such as wind and solar energy have presented certain obstacles to the effective utilization of renewable energy. In order to maximize the utilization of renewable energy, enhance its utilization efficiency, and reduce the carbon emission of power supply, this paper first proposes a real-time collaborative optimal energy scheduling and dispatching control model for multi-microgrids. Subsequently, it proposes a real-time optimal control and dispatching strategy for multi-microgrid energy based on storage collaborative. This model considers the energy storage device as an energy management controller, enabling it to participate in the energy collaborative dispatch of multi-microgrid. This enables the energy storage device in the microgrid not only to actively participate in the optimal scheduling of energy, but also to make the energy in the storage device be reasonably distributed in the multi-microgrid system, to maximize the full absorption of renewable energy in the multi-microgrid, thereby reducing the dependence of the microgrid on the distribution network, and significantly improving the elasticity and reliability of the multi-microgrid system. Finally, the feasibility and effectiveness of the proposed optimal control scheduling strategy are verified by simulation. [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 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 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]