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The author proposes a concept called the Energy Internet as a means of allowing new participants dubbed Energy Cells and Utility Cells to fully engage in a dynamic and diverse energy market through a distributed decision-making process in which electricity rates are determined via competitive and intelligent purchasing and selling strategies. To realize this vision, the underlying distribution infrastructure must change significantly from today's system. [ABSTRACT FROM AUTHOR]

Highlights: [•] Propose a retail electricity market framework in future residential distribution systems with distributed energy suppliers. [•] Identify the key features and functionalities of the proposed retail electricity market framework. [•] Formulate the key market participants (Energy Cell and Utility Cell). [•] Apply novel game-theoretic algorithms to clear the retail electricity market price. [ABSTRACT FROM AUTHOR]

Microgrids are a promising technology that can increase the reliability and economics of energy supply to end consumers. Microgrid development is shifting from prototype demonstration and pilot projects to full-scale commercial deployment. Microgrid energy management systems are critical components that can help microgrids come to fruition. [ABSTRACT FROM AUTHOR]

Abstract: There is a growing need to address the potential problems caused by the emergence of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) within the next 10years. In the near future, a large number of PHEVs/PEVs in our society will add a large-scale energy load to our power grids, as well as add substantial energy resources that can be utilized. The large penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. The existing parking infrastructure is not ready for the large penetration of plug-in vehicles and the high demand of electricity. Nowadays, the advanced computational intelligence methods can be applied to solve large-scale optimization problems in a Smart Grid environment. In this paper, authors propose and implement a suite of computational intelligence-based algorithms (e.g., Estimation of Distribution Algorithm, Particle Swarm Optimization) for optimally managing a large number of PHEVs/PEVs charging at a municipal parking station. Authors characterize the performance of the proposed methods using a Matlab simulation, and compare it with other optimization techniques. [Copyright &y& Elsevier]

Power quality issues in weak grids with large impedance pose significant challenges that limit wind energy (WE) penetration levels and the performance efficiency of existing WE infrastructure. The presence of non-linear (NL) loads at the point of common coupling (PCC) further restricts these levels. This paper addresses these challenges by introducing an additional distributed static compensator (DSTATCOM) at the PCC, controlled by a higher-order Delayed Least Mean Fourth (DLMF) algorithm. The proposed DLMF control algorithm estimates the active and reactive components of the load current by updating their respective weights with appropriate delays, considering variations in loads, DC-link voltage, and wind energy generation. The MATLAB implementation of the proposed control is designed and validated through experimental investigations. These investigations involve varying short circuit ratios, wind speeds, and the presence of NL loads. The results demonstrate that the proposed method can enhance wind penetration levels in weak grids by up to 30%. • Achieved 30% WE penetration in a weak grid with SCR of 2.74, 40% nonlinear load, and 7.5-15 m/s wind speed, as per the international standards. • DLMF control reduces DSTATCOM capacity by 80%, enhancing reactive power management efficiency. • First application of Delayed LMF algorithm for DSTATCOM control in high WE and weak grids. • Through THD analysis shows the DLMF algorithm's effectiveness in improving power quality in weak grids. • Pioneering DLMF-controlled DSTATCOM study offers new insights for high WE penetration in weak grids. [ABSTRACT FROM AUTHOR]

This paper proposes a game-theoretic framework for economic operations of future residential distribution systems with high participation of distributed electricity prosumers. Recently, the large-scale grid integration of distributed renewable energy is becoming a promising solution to restructuring the current power grid infrastructure and ensuring the reliability of energy supply. The wide adoption of distributed renewable energy mainly depends on the participation level of residential consumers and prosumers. Accordingly, there is an urgent need to investigate the economic operations of this new, highly complex retail electricity market that potentially has millions of interactive endpoints. This paper identifies the new roles of utilities and distributed electricity prosumers in the future retail electricity market. The game-theoretic algorithms used to clear the retail electricity market price consider the group coalition scenarios of multiple electricity prosumers. Several numerical case studies are conducted to illustrate and validate the proposed framework. [ABSTRACT FROM AUTHOR]

This paper basically concentrates on providing an appropriate distributed-based energy management framework in smart islands. Smart island is defined as insular territory with the ability to implement integrated solutions to the management of infrastructures and natural resources. The distributed optimization is accomplished by using primal-dual method of multipliers which has shown more promising performance in terms of execution time and convergence rate compared to the alternating-direction method of multipliers. The proposed energy management scheme is carried out between 5 different agents including an energy hub, a networked multi-microgrid with 3 agents and a transportation system. The transportation system comprises of the subway system and plug-in electric vehicles. The proposed networked multi-microgrid is made up of 3 microgrid systems, each one comprises of wind generation units, photovoltaic units and a tidal unit. Despite the assumption that the agents are both suppliers and consumers of energy and are completely operated and separated from the other sections, they share energy through a peer-to-peer energy trading. Such sharing scheme will be ended up by obtaining an equilibrium point through which a consensus is reached and all the parties are satisfied. Results prove the validity of the proposed approach in providing a proper energy negotiation framework in a smart island in terms of accuracy and applicability. • Developing an effective transportation system model within the Smart Island. • Providing a structure for the smart island which comprises of EH, TS and NMMG. • Management of thermal, gas, electrical and water demands of Smart Island. • Investigating fully distributed energy trading scheme among Smart Island agents. [ABSTRACT FROM AUTHOR]

• A TS-based SCUC model is proposed which covers the DUs owned unpredictable events. • An effective TSIRC approach is proposed which results in several advantages. • Uncertainty modelling, increasing WPES profit, reducing generation cost resulted. • Address the impact of price-maker participant on system operation in worse-case. • The problem is formulated as‌‌ max-min-max formulation to get more sensible results. This paper proposes a stochastic transmission switching integrated interval robust chance-constrained (TSIRC) approach to assess the operation of a wind park-energy storage system (WPES) in a day ahead electricity market considering the system technical constraints. To this end, the WPES is assumed as a price-maker, making it possible to alter the market price based on its own profit. The problem formulation is constructed as a tri-level max-min-max structure during a 24-h time horizon. The first level maximizes the hourly profit of the WPES. In the second level, the system operation cost is minimized in the form of a security constrained unit commitment (SCUC) in which the contingency effect associated with the generation units and transmission lines (TLs) as well as the congestion of the TLs is modelled. Considering the independent system operator (ISO) preferences, the third level is dedicated to the robustness of the WPES, which would maximize the allowable output variation of the wind turbines. The proposed tri-level model is then formulated in an efficient bi-level structure using the Karush-Kuhn-Tacker (KKT) conditions. Since the operation of the wind turbine is inherently associated with uncertainty, the TSIRC approach as an effective tool is able to model the uncertainties of the wind units by increasing the WPES profit as a strategic producer while reducing the system operation cost. In addition, unscented transform (UT) as an effective tool is considered to model the uncertainty of the stochastic parameters. The performance evaluation of this work is assessed on an IEEE test system. Different case studies are provided which show the authenticity and effectiveness of the proposed model. The simulation results show that the proposed TSIRC has effectively reduced the amount of lines' switching, and increased the power output of the wind park by 95% on average compared to the conventional TS-based model. In addition, the robustness of the system has increased, and the TSIRC has led the WPES's profit to increase by almost 11.5% compared to the TS-based approach. [ABSTRACT FROM AUTHOR]

In an electricity market with time-varying pricing, uncontrolled charging of energy storage systems (ESSs) may increase charging costs. A novel battery charging control methodology based on reinforcement-learning (RL) is proposed in this paper to minimize the charging costs. A significant characteristic of this method is that it is model-free, with no need for a high-accuracy battery/ESS model. Therefore, it overcomes the challenges brought by limited types of battery models and non-ignorable parametric uncertainties in reality. Additionally, since an accurate prediction of fluctuating electricity prices can promote the control performance, a long short-term memory (LSTM) network is leveraged to improve the prediction precision. The final control objective is to seek an optimal charging portfolio to minimize charging costs. Moreover, the presented control algorithm provides a basic framework for a more complicated electricity market where various types of ESSs, generators, and loads exist. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources and the consequent integration of such distributed energy generation systems into the grid have significantly increased the interaction between power sources, which can result in low frequency oscillations. Different control techniques are proposed in literature to suppress low frequency oscillations, but the first step to attenuate such oscillations is to characterize them in terms of their dominant modes. This paper combines different results from literature to propose a unified two-step methodology for the mode characterization of low frequency oscillations based on the signals acquired by wide area measurement systems. Since the measured signals typically contain noise, the first stage of the proposed method uses the basis pursuit denoising method combined with a Tunable Q-factor wavelet transform to increase the signal-to-noise ratio and improve the identification results. In a second stage, an improved version of the matrix pencil algorithm, as proposed in this paper, is used to identify the parameters of low frequency oscillation dominant modes. Both simulation and experimental results show that the proposed method has better characterization accuracy than traditional methods, especially when Gaussian noise is considered in the measurements. In addition, the processing time has proven to be reasonable for online identification and characterization of low frequency oscillations. [ABSTRACT FROM AUTHOR]