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The locations of marine ranch and its surrounding islands are far away from the mainland, so the cost is too high to keep power supply by laying submarine cables from the mainland. Therefore, it is necessary to build a land-sea relay networked microgrids with high proportion of renewable energy, which is able to provide multi-energy complementary and reliable power for marine ranch and its surrounding islands. It is noteworthy that the networked microgrids is a typical cyber-physical system. In order to deeply reveal the cyber physical fusion characteristics of fishery networked microgrids, the morphology and key techniques of fishery networked microgrids are summarized from two aspects: physical system and cyber system. And the interactions of cyber and physical system are described, including the support from cyber system for the physical system as state perception and control, and the power support from physical system for cyber system. Based on the above interactions, the related research on coupling analysis of cyber-physical system is summarized. Finally, some urgently studied problems have been looked forward to in fishery networked microgrids cyber-physical system.

As a constrained optimization strategy, model predictive control (MPC) is widely used in the optimal dispatch of microgrids. Rolling optimization and feedback correction strategy can effectively deal with the impact due to the system uncertainty and model mismatch which ensures the stable operation of the microgrid. Due to the uncertainty of distributed power sources, MPC can be applied to solve the problem of renewable energy output in microgrid for fishery. This paper reviews the optimal dispatch of microgrid for fishery based on model predictive control. Firstly, this article introduces the basic principles and classification of model predictive control, and then explains the remaining problems and research directions. The characteristics of the microgrid optimal dispatching problem are described, and the analysis are focused on how MPC solves the uncertain problems in the microgrid optimal dispatching. Finally, perspectives and the future research direction of model predictive control in microgrid energy dispatch is reviewed.

The islands and offshore fish farming platforms in the deep sea can form a land-sea networked fishery microgrids to realize the complementary supply of energy. The fishery microgrid contains a high proportion of renewable energy sources such as wind, solar and waves, and mixed AC and DC loads such as feed machine, energy storage, and fishery robot. Due to the harsh operating environment, the possibility of equipment damage increases, and the requirements for system stability are higher. In this paper, firstly, the definition of transient stability of land-sea networked fishery microgrids is given. Then, the key features of the fishery microgrid is introduced, and the influence of the microsource interface type, control strategy, current limiting mechanism and load characteristics on the transient stability is expounded. The definition of power Angle of fishery microgrid and the characterization of instability system are given. Next, the research trends of transient stability are sorted out, evaluated and discussed from two aspects: approaches of transient stability analysis and methods to improve transient stability. Finally, the key issues that need to be solved urgently in the research on the transient stability of the land-sea networked fishery microgrids are summarized, and the relevant research directions are prospected.

The distributed Volt/Var control solution is a promising one for active distribution networks (ADNs), but a critical issue is how to improve the distributed algorithms convergence with less communication burden. In this paper, we propose a bi-level consensus alternation direction multiplier method-based fully distributed Volt/Var optimization algorithm (B-FADMM) for ADNs by exploiting inverter-based rapid control devices. In the first level, the Volt/Var optimal solution of subpartitions is obtained in parallel, and the second level completes the variable increment of each partition to correct the search direction of the first level. In the algorithm, firstly, an adaptive matrix initialization strategy is proposed to adapt to the different magnitude of variables, which avoids non-convergence or slow convergence of the algorithm. Secondly, the null-space method is used in the second level of the algorithm to obtain the null-space basis matrix on the active constraint of the subpartition, which reduces the dimensions of the problem and the computational complexity. In addition, the conjugate gradient (CG) algorithm is used to update the dual multiplier of each partition only by exchanging coupling information among partitions, which protects information privacy.

The existing three-phase imbalanced power flow calculation models for isolated micro-grid do not consider the non-smooth constraints such as voltage control limits and dead zone characteristics, which makes the power flow calculation results cannot meet the actual model constraints. In this paper, a more robust projected Levenberg-Marquardt (PLM) method is used to solve the problem after the non-smooth constraints are converted into smooth functions, which can reduce the model dependence on the initial values and solve the convergence problem caused by non-smooth constraints. The numerical experiments of micro-grid show that considering the non-smooth constraints can effectively improve the accuracy of power flow calculation. In addition, the PLM method has better convergence dealing with non-smooth constraints even poor initial value settings.

Doubly Fed Induction Generator (DFIG) and Permanent Magnet Synchronous Generator (PMSG) are converter-interfaced wind turbines, which have much more complex short circuit behaviour after grid voltage drops. The research on short circuit current contributions provided by the wind turbines is very important for power system planning and relay protection's setting. Here, the Low Voltage Ride Through (LVRT) test data in north China are used to study the short circuit behaviour of DFIGs and PMSGs. The characteristics of two featured quantities, fundamental frequency component and peak current, are investigated. Unlike simulation or theoretical analysis, the field test data of real wind turbines from different manufacturers help utility operators to better understand the fault characteristics of converter-interfaced wind turbines.

This paper introduces a robust sparse recovery model for compressing bad data and state estimation (SE), based on a revised multi-stage convex relaxation (R-Cap-ped-L1) model. To improve the calculation efficiency, a fast decoupled solution is adopted. The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements. The robustness and the computational efficiency of the R-Capped-L1 model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.

In the context of global energy transformation, it is particularly important to tap the potential of flexible loads on the distribution network side to participate in the regulation and operation of the power grid. Effective and reliable load aggregation technology is the basis and prerequisite for flexible loads to participate in the power grid dispatching and control. In order to better understand the current research level of flexible load aggregation technology and make full use of existing research results for further research, this paper summarizes the application of distribution network flexible load aggregation technology in power systems and models various flexible load aggregation. The method and principle are systematically summarized and analyzed, and the future research directions of flexible load aggregation technology are prospected.

In China, large-scale wind power are usually integrated to the power system via long transmission lines, which is one of the most important development and utilization mode. With the increasing of installed capacity in wind power integration area, power quality problems, especially voltage unbalance rise. In this paper, an improved automatic voltage control (AVC) framework is proposed and the three-phase reactive power optimization model is developed considering wind power integration. Therefore, the goals of voltage profile and negative-sequence voltage depression are achieved. Finally, the efficiency are verified via a case study in North China.

Many autonomous microgrids have high penetration of distributed generation (DG) units. Optimal power flow (OPF) is necessary for the optimal dispatch of such networked microgrids (NMGs). Existing convex relaxation methods for three-phase OPF are only applicable to radial networks, not meshed networks. To overcome this limitation, we develop a semidefinite programming (SDP) convex relaxation model, which can be applied to meshed networks and also includes a model of three-phase DG units and on-load voltage regulators with different connection types. The proposed model has higher accuracy than other existing convex relaxation models and the SDP model effectively solves the OPF problem for three-phase meshed networks with satisfactory accuracy, as validated by real 6-bus, 9-bus, and 30-bus NMGs and the IEEE 123-bus test cases. In the SDP model, the convex symmetric-component of the three-phase DG model is shown to be more accurate than three-phase DG that is modelled as three single-phase DG units in three-phase unbalanced OPF. The optimal control variables obtained from the convex relaxation optimization can be used for both the final optimal dispatch strategy and the initial value of non-convex OPF to obtain the globally optimal solution efficiently.

Regardless of the rapid development of wind power capacity installation around the world, wind curtailment is a severe problem to be solved. Wind curtailment can cause abundant outliers and change the original characteristics of operation data in wind farms. Power curve cannot be accurately modeled with these outliers and consequently wind power forecasting as well as other applications in power system will be negatively affected. In this paper, the characteristics of the outliers caused by wind curtailment are analyzed. Then, a data-driven outlier elimination approach combining quartile method and density-based clustering method is proposed. First, the quartile method is used twice for eliminating sparse outliers. Then density-based spatial clustering of applications with noise method is applied to eliminate stacked outliers. A case study is carried out by modeling the power curves of a wind farm and 20 wind turbines in this wind farm. The accuracy of power curve modeling is significantly improved and the elimination procedure can be completed in a very short time, indicating that the proposed methods are effective and efficient for eliminating outliers. The performance of the methods is insensitive to their parameters and can be directly used in different cases without tuning parameters, both for wind turbines and wind farms.

With the increasing penetration of wind power into the electricity grid, wind power forecast error analysis plays an important role in operations scheduling. To better describe the characteristics of power forecast error, a probability density function should be established. Compared with the Kernel density estimation method, this paper adopts the Gaussian mixture model (GMM), which is flexible enough to capture different error distribution characteristics, such as bias, heavy tail, multi-peak, and so on. In addition, for GMM parameter estimation, when dealing with a large number of multi-dimensional data sets or unbalanced overlapping mixtures, the expectation maximization (EM) algorithm shows a slower convergence speed and requires a high number of iterations. In this paper, a new L-BFGS optimization method, based on the Riemannian manifold, is used for GMM parameter estimation. Based on actual wind power forecast error data, the suitability of the model and the new optimization algorithm was verified in large, multi-dimensional data sets. The new optimization algorithm has fewer iterations than the EM algorithm, with an improved convergence speed.

Linear power flow (LPF) is necessary for robust and fast centralized control of active distribution networks (ADNs). With penetration of distributed generators (DGs) into ADN, voltage-controlled nodes are becoming more common, to maintain a normal voltage profile in the distribution network. Existing three-phase LPF formulations under a rectangular coordinate system cannot cope with local voltage-controlled nodes, and does not consider multi-slack-node features of three-phase distribution network, detailed control characteristics and loss participation features of DGs. Here, a general three-phase LPF under a polar coordinate system is presented to address these issues. The proposed method can account for various connection ZIP loads, transformers, and single-phase or three-phase DGs. The detailed control model of the DGs and the distributed slack bus are taken into account. The effectiveness and advantages of the proposed method are validated with balanced 33, 70, 84, 119, and 874-node networks and modified IEEE 13, 34, 37, and 123 unbalanced networks.

The composition of worldwide energy consumption is undergoing tremendous changes due to the consumption of non-renewable fossil energy and emerging global warming issues. Renewable energy is now the focus of energy development to replace traditional fossil energy. Energy storage system (ESS) is playing a vital role in power system operations for smoothing the intermittency of renewable energy generation and enhancing the system stability. We divide ESS technologies into five categories, mainly covering their development history, performance characteristics, and advanced materials. Biomass storage and gas storage are also discussed, which are not considered in most reviews. After detailed research, the rapid development of each technology in recent years is introduced, and some representative research works are surveyed. We comprehensively summarized the advantages and disadvantages of various ESS technologies and presented several evaluation indicators for quantitative analysis. Hybrid ESS is also considered based on the complex market demand. Then, we investigate the applications of various ESS technologies as short-term, medium-term, and long-term storages in power systems, covering the power generation, transmission and distribution, and end-user. Finally, this paper reviews global developing trends, and identifies critical challenges and promising opportunities.

Unbalanced load flow solution provides the initialisation of electromagnetic transient (EMT) simulation model for the unbalanced active distribution network. The most electro-magnetic transient program (EMTP) software use the d–q-axis-based synchronous machine's parameters, so the existed sequence component-based model is not suitable for the EMT model initialisation. In this study, an unbalanced steady-state synchronous machine model is presented which is derived from the original dynamic model and its parameters are consistent with EMT dynamic model. It is more precise than the sequence component parameters based model. Furthermore, the saturation phenomenon of main flux is also considered in this model. The proposed model is validated and compared with Modelica simulation results.

In the conventional power flow (PF) calculation, when using the PV-PQ bus switching to deal with the problem of the generator buses reactive power limits violation for power systems, this method may cause the power flow calculation to fail. If using the Fischer-Burmeister (FB) function to handle constraint exchange issues, then using the Newton-Raphson (NR) method to solve PF, this operation will lead to a slow convergence. In this paper, a semi-smooth projected Levenberg-Marquardt (PLM) power flow method based on the FB function to deal with the constraints exchange issues is proposed. This method can change the search direction when the constraint exchange positions are at sharp corners, and adjust the size of the trust region to prevent the power flow from divergence due to excessive step size in the iterative process, thus improving convergence and reliability. Furthermore, the traditional power flow calculation method is sensitive to the initial value, which may cause the PF divergence, however the method of PLM proposed in this paper is globally convergent, which is not sensitive to the initial value. Numerical tests implemented on a small 3-bus network and a modified IEEE 118-bus system show that the proposed method is more efficient and robust than existing methods.

In weak power system with large-scale wind power integration, three-phase unbalance occurs frequently. Voltage unbalance may lead to more losses and heat to power system and deteriorate operation performance of wind turbine generators. In this paper, the voltage unbalance mechanism is investigated based on a field test case in North China. The technical features of three-phase voltage are extracted from field test data. An equivalent three-phase model is proposed, based on which, the theoretical derivation of voltage unbalance factor(VUF) is investigated and impacts of wind power on VUF are analyzed. Finally, the field practice model is built on matlab/simulink platform and the simulated three-phase voltage and VUF are compared with field test data.

This paper presents a new unbalanced steady-state model of doubly fed induction generator (DFIG) for the three-phase load flow analysis. The existing unbalanced DFIG steady-state model does not consider the effects of different rotor speeds. However, the steady-state model of DFIG changes significantly when it operates at supersynchronous or subsynchronous speeds. In this paper, a new unbalanced steady-state model for DFIG operating at different speeds is discussed in detail. Furthermore, the positive-sequence active power in this model is simulated more precise than the existing DFIG models. The proposed model is validated on CIGRE six-bus test system and can be used to initialize the transient simulation for unbalanced active distribution networks.

With the increasing demand-side participation in electricity market, as a profit-seeking market participant, load-serving entities (LSEs) have been trying to apply demand response (DR) programs to induce the demand elasticity to further their profit. However, due to the different preference of DRs, it is difficult for LSEs to generate the optimal strategic bidding strategy considering DR in the ISO/RTO’s market. Therefore, this paper proposed a bi-level optimization model with the consideration of demand response bidding to maximize the total profit of LSEs: 1) conceptually, different from previous related works, the consumers participate DR through setting their bidding prices to LSEs with respect to their own preference and LSEs should determine the optimal reward value of DR as well as the amount of demanded electricity; and 2) technically, an original method has been implemented to solve the bi-level optimization model. The closed form of shadow price function with respect to the total load demand is derived to reduce the complexity of the proposed bi-level model. Hence, the proposed model is converted to a mixed integer second order cone programming and able to achieve the global optimality. It needs to be note that the closed form of shadow price introduced in this paper can also be applied to other bi-level programming models. Moreover, case studies have been performed to demonstrate the validity of the proposed method: 1) the proposed method to obtain the closed form of real-time price is verified on a 9-bus system; 2) 118-bus test system with three demand response participants is tested to show that by the proposed method, LSE can benefit from the DRs under various circumstance.

With the increase in the capacity of nuclear power, wind power and other new energy sources connected to the power grid, the pressure on peak load regulation of power systems in some areas has increased, such as Fujian Province. It is imperative to study the unit combination (UC) problem with multiple power types participation. In this paper, we first establish the total cost objective function including fuel cost, start-stop cost and peak load regulation cost for thermal and nuclear power units. In the constraints, considering the operational constraints of various unit types, the safety constraints of the line network and the impact of uncertainty on the spare capacity of the system. In addition, an energy storage system model has been added. The influence of uncertainty on the spinning capacity of the system is mainly caused by the wind power forecast error. By introducing the chance constraint on spinning reserve, avoiding the over-allocation of the reserve, effectively. Then, a UC model involving multiple power types, like nuclear power, thermal power, wind power, and large-scale energy storage system is established. Finally, the feasibility of the UC model in the power system pre-dispatching is verified in the IEEE RTS 24-bus system.

Fast decoupled state estimation (FDSE) is proposed for distribution networks, with fast convergence and high efficiency. Conventionally, branch current magnitude measurements cannot be incorporated into FDSE models; however, in this paper, branch ampere measurements are reformulated as active and reactive branch loss measurements and directly formulated in the proposed FDSE model. Using the complex per unit normalization technique and special chosen state variables, the performance of this FDSE can be guaranteed when it is applied to distribution networks. Numerical tests on seven different distribution networks show that this method outperforms Newton type solutions and is a promising method for practical application.

Linear state estimation (SE) formulation play an important role in real-time distribution network management. The voltage magnitude, active power, and reactive power measurements are transformed to linear measurements with large initial node voltage errors when SE under a rectangular coordinate system. In order to solve this problem, a linear state estimation under a polar coordinate system is proposed to obtain a sufficiently accurate complex node voltage in the first stage, and then nonlinear measurements are transformed to linear measurements with the complex node voltage. Bad data identification can be embedded in this stage. In the second stage, linear SE under a rectangular coordinate system are used. The proposed two-stage linear SE is validated on balanced 33, 70, 84, 119, 874-node distribution networks and IEEE 13, 34, 37, 123 unbalanced test feeders.

The analysis of active distribution networks is the basis of Energy Internet control and management. As the penetration of distributed renewable energy into distribution networks has increased, traditional applications, such as OpenDSS, are not suitable for analyzing networks that contain distributed generators (DGs). For distribution network calculation, three-phase DGs are often modeled in sequence frame, while the three-phase unbalanced distribution networks are usually modeled in phase frames. Hence, this paper represents a novel model that could deal with both frames by load flows in a sequence-phase coupled frame. The steady state analysis model presented in this paper is implemented using an open source language called Modelica. Tests on the IEEE testing systems are carried out and the results demonstrate the advantage of the model.

Large-scale distributed generators (DGs) penetrated into the active distribution network (ADN) impact the voltage profile significantly. Autonomous voltage control strateges simulation becomes an crucial issue in the ADN steady state analysis. Conventional Newton-Raphson or fixed point iteration based power flow convergence failure is frequently incurred by heuristic Q-limit adjustment of DGs and voltage band regulation of step voltage regulator (SVR). To cope with these issues, mixed complementarity problem (MCP) is formulated to model the Q-limit issue of voltage controlled DGs and voltage regulation of SVR etc.. To improve the convergence of the proposed method, nonlinear equations are linearized with Newton algorithm and then fast Lemke algorithm are utilized to solve the linear complementarity programming model (LCP). The effectiveness and better performance of the proposed method under operation off-limit of DGs and tap adjustment of SVRs were validated using unbalanced IEEE 4, 34, 123 test feeder and other large practical three-phase network test case.

Linear state estimation (SE) formulation under a rectangular coordinate system has been proved to be applicable for real-time distribution network management. Micro phasor measurements’ model can be accommodated into this kind of SE easily. However, voltage magnitude, active power and reactive power measurements are transformed to linear measurements with large node voltage error. To cope with this issue, a linear state estimation under a polar coordinate system is adopted at the first stage to obtain accurate enough complex node voltage, and then nonlinear measurements are transformed to be linear with complex node voltage. At the second stage, linear SE under a rectangular coordinate system can be adopted to satisfy more strict network constraints. The proposed two-stage linear SE is validated on balanced 14, 33, 70,84, 119, 135 nodes network and IEEE 13, 34, 37,123 unbalanced test feeders.

With large-scale distributed generators (DGs) in an active distribution network (ADN), conventional load flow convergence failure is incurred by heavy power transmission. The Holomorphic Embedding Load Flow Method (HELM) has proven to be more robust than the Newton–Raphson method under heavy power transmission. At present, HELM is mainly designed for balanced transmission networks. In this study, we developed a three-phase HELM model to accommodate DGs, delta connection loads, and ZIP loads for ADN. The effectiveness and better performance of the proposed method under heavy load situations were validated using modified unbalanced IEEE 13, 34, 37, and 123 test feeders.

The optimal power flow (OPF) problem for active distribution networks with distributed generation (DG) and a variety of discretely adjustable devices (e.g., on-load tap-changers, OLTCs) is essentially a non-convex, nonlinear, mixedinteger optimization problem. In this paper, the quadratic model of three-phase OLTCs is proposed by adding branch currents as unknown variables, which guarantee a constant Hessian matrix throughout iterations. This paper proposes a three-phase OPF model for active distribution networks, considering a three-phase DG model. The OPF model is solved by an interior point method incorporating a quadratic penalty function as opposed to a Gaussian penalty function. Furthermore, a voltage regulator is also incorporated into the OPF model to form an integrated regulation strategy. The methodology is tested and validated on the IEEE 13-bus three-phase unbalanced test system.