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Multi-terminal hybrid AC/DC power systems have found a wide application in different areas, such as wind and solar energy, microgrids, shipboard power systems (SPS), etc. To ensure reliability and survivability of the SPS, the emergency control is necessary to be implemented, which is widely utilized in conventional AC networks and hybrid AC/DC systems. This paper proposes a concept of reconfiguration theory based on a centralized emergency control algorithm of the SPS with a complex structure, which has high requirements for reliability and survivability. The proposed method complements the well-proven local control approaches, paying particular attention to the SPS reconfiguration. A special emphasis is placed on improving the SPS reliability by combining the AC nodes, as well as on increasing the reconfiguration procedure efficiency, the latter being achieved by not taking into account the special constraint on the DC circuit breakers states. The algorithm carries out a preventive calculation of control actions using the genetic algorithm optimization procedure. The case studies were performed by using the modified DC zonal electrical distribution system (DC-ZEDS) model, and the results verify the feasibility and effectiveness of the proposed method and model. [ABSTRACT FROM AUTHOR]

We propose the generalized quadrature methods for numerical solution of singular integral equation of Abel type. We overcome the singularity using the analytic computation of the singular integral. The problem of solution of singular integral equation is reduced to nonsingular system of linear algebraic equations without shift meshes techniques employment. We also propose generalized quadrature method for solution of Abel equation using the singular integral. Relaxed errors bounds are derived. In order to improve the accuracy we use Tikhonov regularization method. We demonstrate the efficiency of proposed techniques on infrared tomography problem. Numerical experiments show that it makes sense to apply regularization in case of highly noisy (about 10%) sources only. That is due to the known fact that Volterra equations of the first kind enjoy selfregularization property. [ABSTRACT FROM AUTHOR]

The energy communities based on the integration of microgrids make it possible to gain economic, environmental, technical, and social benefits. The paper aims to propose a unified multi-criteria approach covering both the planning stage and the stage of managing the energy community, in the context of various interests of its participants. Planning stage should take into account the long-term goals of the community and possible changes in external conditions. Therefore, we suggest an approach relying on the multi-attribute value theory considering the uncertainty of decision makers' preferences. Interval estimators used to express preferences enable a choice of community configuration with robust performance under changing conditions within some limits. In the operation stage, the new multi-criteria model of an intelligent "energy community operator" is proposed. It is based on bi-level programming and reinforcement learning, implementing the structure of a fair local market for sustainable development of the community. To optimize the operation of individual microgrids within the community, the multi-objective Monte-Carlo Tree Search (MCTS) algorithm is used, which helps to improve the convergence in the Stackelberg game. The multi-criteria version of the MCTS algorithm allows implementing an adaptive local automation model to solve a multi-objective lower-level problem: minimize operating costs, risk of power shortage, and CO2 emissions; smooth load peaks, and optimize power exchange between microgrids. At the top level, a management strategy that will be beneficial to all members of the community is chosen to guarantee their long-term aggregation. The effectiveness of the proposed approach is demonstrated by the example of an energy community created for three remote villages located on the coast of the Sea of Japan. The natural and climatic conditions of the area allow the efficient use of wind, solar, and biomass resources. Building the community involves the consideration of three scenarios, in which priority is given to economic efficiency, environmental efficiency, or balanced development. • The practical multi-criteria approach for development a sustainable energy community. • Energy community sizing under uncertainty of decision makers' preferences. • Bi-level programming for a "fair" community operator model. • Monte-Carlo Tree Search algorithm for multi-criteria community management. [ABSTRACT FROM AUTHOR]

Energy communities is a new, but already successful prosumer model of the local energy systems' construction. It is based on distributed energy sources and the electricity consumers' flexibility who are the members of the community. In search of the most effective ways to interact within themselves and with the external energy system, local energy communities become platforms for exciting experiments in the field of new energy practices including local markets for flexibility, building cooperative microgrids, achieving energy autonomy, and many others. This work aims to present a unified approach to building and optimally managing the community microgrids with an internal market, given the social, environmental, and economic benefits of a particular location of such a community. A new modeling framework is introduced, based on bilevel programming and reinforcement learning, for structuring and solving the internal local market of a community microgrids, composed of entities that may exchange energy and services among themselves. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator (Community EMS). We strengthen the traditional bilevel problem statement by the local energy management system (Local EMS) introduction based on Monte-Carlo tree search algorithm. Our approach makes it possible to enable interaction of the local control systems for microgrids with the community microgrid operator as part bilevel programming problem solution. Numerical results obtained on the real test case of the microgrid community for the settlements located in the Transbaikal National Park (Russia), which include various renewable energy sources (wind, solar power, biomass gasifiers) and storage devices, show reduction of the LCOE index from 20% to 40% and improving the quality of electricity supply to the analyzed settlements. • Methodology for economically optimised design a community microgrids based on RESs. • The concept of building green microgrids communities integrated with biomass gasifiers. • The concept of a fair local electricity market using bi-level optimization. [ABSTRACT FROM AUTHOR]

• A power interval prediction model is established based on LSTM and LUBE to quantify the uncertainty of wind power. • The energy storage management is transformed into Markov decision process and solved by deep reinforcement learning. • According to the real-time state, the proposed strategy can make the charge/discharge schedule automatically. Wind power generation combined with energy storage is able to maintain energy balance and realize stable operation. This article proposes a data-driven energy storage management strategy considering the prediction intervals of wind power. Firstly, a power interval prediction model is established based on long-short term memory and lower and upper bound estimation (LUBE) to quantify the uncertainty of wind power, which solves the issue that traditional LUBE cannot adopt gradient descent method. Secondly, the energy storage management is transformed into Markov decision process and solved by deep reinforcement learning. The state space, action space and reward function of the interaction between agent and environment are established, and the value function is approximated through the deep Q network. Then, according to the real-time state, such as wind power, power prediction intervals, local load, dynamic electricity price and state of charge, the proposed strategy can make the charge/discharge schedule automatically. Finally, the effectiveness and superiority of the proposed energy storage management strategy are verified based on real wind farm dataset. The proportion of wrong decisions is zero, and daily transaction cost and wear cost of energy storage management system decrease significantly. [ABSTRACT FROM AUTHOR]

The short-term forecasting of wind power, photovoltaic (PV) generation and loads is important for the secure and economical dispatching of smart community with smart grid. Considering the smart community has plenty of distributed generations, here, a concept of net load is defined as the active power difference between renewable generations (wind and PV power) and loads. Then, a combined forecasting approach, which enables to build a real-time forecasting model with parameters self-adjustment, is proposed for the forecasting of the net load in smart community. Compared with the traditional forecasting methods such as support vector machine (SVM), the proposed approach can wavily optimize the parameters of the forecasting model. Besides, an optimal method named Grid-GA searching is developed to reduce the computation time during the forecasting. Therefore, it can improve the forecasting accuracy even if there is a great of uncertainty component in wind power, PV generation and loads. Detailed case studies give a contrastive analysis of the traditional and the proposed forecasting approach. The results show that the proposed approach has the capability of self-adaption on the fluctuations of wind and PV power, and can effectively improve the forecasting accuracy and efficiency. [ABSTRACT FROM AUTHOR]

To overcome the limitation of the existing topologies and control strategies of SST, a modular multilevel converter type solid state transformer (MMC-SST) with internal model control method is proposed in this paper. First, the structure and operating characteristics of MMC-SST are analyzed, and accordingly its mathematical model of input- and output-stage under synchronous rotating coordinate is established. Secondly, a new kind of dual-loop control structure combine with the internal model control (IMC) current inner loop and proportional integral (PI) voltage outer loop is developed, according to the characteristics of IMC. Lastly, a simulation model is established and the typical operating cases, i.e., the grid side voltage variation, load change, grid and load side power factor deviation, are simulated. Results show that the internal model control based MMC-SST can run stable under the required power factor, and the good performances such as fast voltage and current response speed, strong anti-disturb ability to load, and enhanced robustness on system operation are demonstrated. [ABSTRACT FROM AUTHOR]

Complicated construction of Lyapunov–Krasovskii functional (LKF) makes the quadratic correlation terms of time-delay appear in its derivative. This paper proposes two relaxed negative-determination lemmas to deduce the negative definite condition of a quadratic function with respect to a time-varying delay, which contain some popularly lemmas as their special cases. Combining a novel augmented LKF and generalized free-matrix-based integral inequality (GFMBII), the developed lemmas are respectively applied to derive the stability criteria for nominal and uncertain systems. Three numerical examples are given to prove the potential gain of the two lemmas and the superiority of the criteria over the previous work. [ABSTRACT FROM AUTHOR]