Abstract:Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.

Abstract:A new inter-cluster DC capacitor voltage balancing scheme for a delta connected modular multilevel cascaded converter (MMCC)-based static synchronous compensator (STATCOM) is presented. A detailed power flow analysis of applying negative sequence current (NSC) and zero-sequence current (ZSC) injection methods in addressing the issue of inter-cluster DC voltage imbalance under unbalance grid voltage is carried out. A control scheme is proposed which integrates both inter-cluster methods using a quantification factor QF. This is used to achieve the sharing of the inter-cluster active power between the NSC and ZSC injection methods. An accurate method of determining the quantification factor is also presented. The proposed method offers better sub-module DC capacitor voltage balancing and prevents converter overcurrent. The influence of unbalanced grid voltage on the delta connected MMCC-based STATCOM rating using this integrated cluster balancing technique is investigated. The control scheme is verified with a 5 kV 1.2MVA MMCC-STATCOM using 3-level bridge sub-modules, and the results show the advantages of the proposed method over other inter-cluster methods.

Abstract:Electric distribution networks have to deal with issues caused by natural disasters. These problems possess unique characteristics, and their severity can make load restoration methods impotent. One solution that can help in alleviating the aftermath is the use of microgrids (MGs). Employing the cumulative capacity of the generation resources through MG coupling facilitates the self-healing capability and leads to better-coordinated energy management during the restoration period, while the switching capability of the system should also be considered. In this paper, to form and schedule dynamic MGs in distribution systems, a novel model based on mixed-integer linear programming (MILP) is proposed. This approach employs graph-related theories to formulate the optimal formation of the networked MGs and management of their proper participation in the load recovery process. In addition, the Benders decomposition technique is applied to alleviate computability issues of the optimization problem. The validity and applicability of the proposed model are evaluated by several simulation studies.

Abstract:Finite control set-model predictive control (FCS-MPC) is employed in this paper to control the operation of a three-phase grid-connected string inverter based on a direct PQ control scheme. The main objective is to achieve high-performance decoupled control of the active and reactive powers injected to the grid from distributed energy resources (DER). The FCS-MPC scheme instantaneously searches for and applies the optimum inverter switching state that can achieve certain goals, such as minimum deviation between reference and actual power; so that both power components (P and Q) are well controlled to their reference values. In addition, an effective method to attenuate undesired cross coupling between the P and Q control loops, which occurs only during transient operation, is investigated. The proposed method is based on the variation of the weight factors of the terms of the FCS-MPC cost function, so a higher weight factor is assigned to the cost function term that is exposed to greater disturbance. Empirical formulae of optimum weight factors as functions of the reference active and reactive power signals are proposed and mathematically derived. The investigated FCS-MPC control scheme is incorporated with the LVRT function to support the grid voltage in fulfilling and accomplishing the up-to-date grid codes. The LVRT algorithm is based on a modification of the references of active and reactive powers as functions of the instantaneous grid voltage such that suitable values of P and Q are injected to the grid during voltage sag. The performance of the elaborated FCS-MPC PQ scheme is studied under various operating scenarios, including steady-state and transient conditions. Results demonstrate the validity and effectiveness of the proposed scheme with regard to the achievement of high-performance operation and quick response of grid-tied inverters during normal and fault modes.

Abstract:This paper formulates and solves a techno-economic planning problem of reactive power (VAR) in power transmission systems under loadings. The objective of the proposed research work is to minimize the combination of installation cost of reactive power sources, power losses and operational cost while satisfying technical constraints. Initially, the positions for the placement of reactive power sources are determined technically. Different cost components such as VAR generation cost, line charging cost etc. are then added in the total operating cost in a most economical way. Finally, the optimal parameter setting subjected to reactive power planning (RPP) is obtained by taking advantages of hybrid soft computing techniques. For the justification of the efficiency and efficacy of the proposed approach the entire work is simulated on two inter-regional transmission networks. To validate the robustness and ease of the soft computing techniques in RPP the responses of benchmark functions and statistical proof are provided simultaneously.

Abstract:Fluctuation evaluation is an important task in promoting the accommodation of photovoltaic (PV) power generation. This paper proposes an evaluation method to quantify the power fluctuation of PV plants. This consists of an index system and a ranking method based on the RankBoost algorithm. Eleven indices are devised and included in the index system to fully cover diverse fluctuation features. By handling missing and invalid data effectively, the ranking method fuses multiple indices automatically and provides a systematic and comprehensive comparison of power fluctuation. Simulation results based on power data from six PV plants indicate that the evaluation list obtained by the RankBoost ranking method is better represented and more comprehensive than that derived by the equal weight method.

Abstract:Time-of-use (TOU) pricing strategy is an important component of demand-side management (DSM), but the cost of supplying power during critical peak periods remains high under TOU prices. This affects power system reliability. In addition, TOU prices are usually applicable to medium- and long-term load control but cannot effectively regulate short-term loads. Therefore, this paper proposes an optimization method for TOU pricing and changes the electricity consumption patterns during the critical peak periods through a critical peak rebate (CPR). This reduces generation costs and improves power system reliability. An optimization model for peak-flat-valley (PFV) period partition is established based on fuzzy clustering and an enumeration iterative technique. A TOU pricing optimization model including grid-side and customer-side benefits is then proposed, and a simulated annealing particle swarm optimization (SAPSO) algorithm is used to solve the problem. Finally, a CPR decision model is developed to further reduce critical peak loads. The effectiveness of the proposed model and algorithm is illustrated through different case studies of the Roy Billinton Test System (RBTS).

Abstract:There are various types of distributed generators (DGs) with diferent grid integration strategies. The transient char‑ acteristics of the fault currents provided by the DGs are diferent to those of conventional synchronous generators. In this paper, a distribution network with multi-type DGs is investigated, including consideration of DG low-voltage ride through (LVRT). The fault current characteristics of two typical DGs, i.e. an inverter-interfaced distributed generator (IIDG) and a doubly-fed induction generator (DFIG), are analyzed, considering the specifc operation modes. Based on analysis of the fault characteristics, an equivalent model of the multi-type DGs under symmetrical/asymmetrical fault conditions is established. A fast-iterative fault calculation method for enhancing the calculation efciency while avoid‑ ing local convergence is then proposed using an improved particle swarm optimization (PSO) algorithm. A simula‑ tion system of the distribution network with multi-type DGs is established in PSCAD/EMTDC. The simulation results validate the high accuracy and calculation efciency of the proposed calculation method of the fault components. This can assist in the settings of the protection threshold.

Abstract:A large portion of the available power generation of a photovoltaic (PV) array could be wasted due to partial shading, temperature and irradiance efects, which create current/voltage imbalance between the PV modules. Partial shading is a phenomenon which occurs when some modules in a PV array receive non-uniform irradiation due to dust, cloudy weather or shadows of nearby objects such as buildings, trees, mountains, birds etc. Maximum power point tracking (MPPT) techniques are designed in order to deal with this problem. In this research, a Markov Decision Process (MDP) based MPPT technique is proposed. MDP consists of a set of states, a set of actions in each state, state transition probabilities, reward function, and the discount factor. The PV system in terms of the MDP framework is modelled frst and once the states, actions, transition probabilities, and reward function, and the discount factor are defned, the resulting MDP is solved for the optimal policy using stochastic dynamic programming. The behavior of the resulting optimal policy is analyzed and characterized, and the results are compared to existing MPPT control methods.

Abstract:The integration of distributed renewable energy sources into the conventional power grid has become a hot research topic, all part of attempts to reduce greenhouse gas emission. There are many distributed renewable energy sources available and the network participants in energy delivery have also increased. This makes the management of the new power grid with integrated distributed renewable energy sources extremely complex. Applying the technical advantages of blockchain technology to this complex system to manage peer-to-peer energy sharing, transmission, data storage and build smart contracts between network participants can develop an optimal consensus mechanism within the new power grid. This paper proposes a new framework for the application of blockchain in a decentralised energy network. The microgrid is assumed to be private and managed by local prosumers. An overview description of the proposed model and a case study are presented in the paper.

Abstract:Because of system constraints caused by the external environment and grid faults, the conventional maximum power point tracking (MPPT) and inverter control methods of a PV power generation system cannot achieve optimal power output. They can also lead to misjudgments and poor dynamic performance. To address these issues, this paper proposes a new MPPT method of PV modules based on model predictive control (MPC) and a finite control set model predictive current control (FCS-MPCC) of an inverter. Using the identification model of PV arrays, the module-based MPC controller is designed, and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature. An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors, the optimal voltage vector is selected according to the optimal value function, and the corresponding optimal switching state is applied to power semiconductor devices of the inverter. The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified, and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink. The results show that MPC has better tracking performance under constraints, and the system has faster and more accurate dynamic response and flexibility than conventional PI control.