Compressed air energy storage (CAES) can balance the fluctuation of renewable generation. In this study, the CAES is used as an energy storage system in the distribution network with wind power generation (WPG). Considering the forecasting error of WPG and load, a robust day-ahead optimal dispatch scheme for the CAES is developed in this study. The characteristics of the CAES are modelled in detail for the expansion/compression operation during the robust optimal dispatch (ROD). The aim is to minimise the fluctuation of the net load in the distribution network. Robust dynamic programming is employed to solve the optimisation problem in this study. The developed scheme is tested with the IEEE-RBTS load profile and real WPG data. The deterministic and ROD-ahead dispatch scheme are simulated and compared. The fluctuation of the net load is compared with different weights of the variation and uncertainty set.

Compressed air energy storage (CAES) can balance the fluctuation of renewable generation. In this study, the CAES is used as an energy storage system in the distribution network with wind power generation (WPG). Considering the forecasting error of WPG and load, a robust day-ahead optimal dispatch scheme for the CAES is developed in this study. The characteristics of the CAES are modelled in detail for the expansion/compression operation during the robust optimal dispatch (ROD). The aim is to minimise the fluctuation of the net load in the distribution network. Robust dynamic programming is employed to solve the optimisation problem in this study. The developed scheme is tested with the IEEE-RBTS load profile and real WPG data. The deterministic and ROD-ahead dispatch scheme are simulated and compared. The fluctuation of the net load is compared with different weights of the variation and uncertainty set.

This paper studies the wind power regulation problem by controlling a population of thermostatically controlled loads (TCLs) in smart grids. A decentralised bi-level control approach is proposed, where the first-level controller achieves load following based on wind power forecast, while the second-level controller deals with the forecast error effectively using the frequency deviation information. Specifically, to realise the fair dispatch of wind power in a users' comfort sense, the wind power following problem is formulated as a quadratic optimisation problem, which is solved by the first-level controller. The second-level adopts a local proportional controller to handle the frequency deviation caused by the forecast errors of wind power. The proposed algorithm converges fast and is decentralised in the sense that the TCLs conduct local computation and keep the parameters' privacy from the aggregator. Simulations are given to show the performance of the proposed approach.
© 2019 Institution of Engineering and Technology. All rights reserved.

Under the vision of high proportion renewable energy power system in the future, the renewable energy (RE) such as wind power/PV with variation and uncertainty, as the main energy supplier, brings great flexibility demand to the power system. Typical model considering only one type of flexible resource and the power planning method based on the net load envelope will no longer applicable in such new complex scenarios. This paper introduces flexibility index to quantitatively evaluate the regulatory potential of the source-load-storage multi-type flexible resources and incorporate into the planning model as an optimal decision variable. Thus, a high-proportion RE planning model with the goal of minimizing the cost of full-cycle investment is established. And through three process modules of production simulation, flexibility evaluation and power investment decision making, the optimal power planning scheme of best flexibility performance can be realized. Finally, case study is proceed basing on the 2050 long term planning of a provincial power grid in North east China, and above model and algorithm is verified. © 2018 IEEE.

Automatic voltage control (AVC) has been widely used in power grid dispatching system. With the high penetration of renewable generation, the reactive power uncertainty of source and load increases the voltage fluctuation, which brings new challenges to AVC. The traditional AVC strategy is based on the linear reactive voltage sensitivity method. When the system topology changes and the model update speed is not fast enough, the accuracy of AVC will be affected. This paper proposes a method for fitting the nonlinear relationship of reactive power and voltage based on historical data by Radial Basis Function (RBF) Neural Network, which can get more precise control even without precise parameters of model. We firstly obtain the voltage and reactive data of each node of the system with the inputs of the known generation and load data based on the simulation system. And then fit the nonlinear relationship between reactive power and voltage, which would replace the traditional secondary voltage control strategy. A 38 nodes sample system are applied to verify the control effects of the proposed method. © 2018 IEEE.

The benefits of transmission-distribution interactions at boundary buses are gradually unlocked in a restructured power network. Since the roles of transmission system planner and distribution system planner have some similarities in maintaining the economy and security of their systems, the transmission and distribution systems should collaborate and coordinate with each other to determine the expansion scheme in a positive and active manner. To address this problem, this paper presents a decomposition based expansion method for hybrid transmission and distribution networks (HTDNs), which simultaneously optimizes the hourly generation schedule. The original centralized optimization problem is decomposed into a transmission expansion subproblem encompassing numbers of distribution expansion subproblems. A distributed solution technique is proposed to guarantee shared variable consistencies. Case studies carried out on a HTDN, denoted as T24D9, validate the effectiveness and high performance of the formulated expansion problem.
© 2019 IEEE.

Economic dispatch problem (EDP) of distributed energy resources (DERs) is the key to achieve balanced coordination and economic operation of renewable energy and conventional generation, which is of vital significance to smart grids. This paper mainly investigates the EDP of DERs with transmission losses. We formulate the lossy EDP as an optimization problem, of which the objective is to minimize the aggregated production cost under physical constraints. It is a great challenge to solve the lossy EDP in a distributed fashion, as the nonconvexity of the transmission loss formula has posed extra difficulties. This paper proposes a consensus-based distributed lambda iteration algorithm to solve the lossy EDP, and a damping mechanism is designed to overcome the oscillatory problem that conventional lambda iterations often suffer. The proposed algorithm is further improved, in order to tackle the prohibited operating zones of generators. Exhaustive simulations are presented to illustrate the proposed algorithm.
© 2019 John Wiley & Sons, Ltd.

High penetration of volatile renewable energy produces uncertainties in power system and poses severe challenges to transmission network expansion planning (TNEP). Usually, only one type of mathematical model of different uncertainties was considered in every single TNEP method. However, in the process of TNEP, different uncertainties may show different mathematical features and need to be represented by various types of mathematical models. Moreover, transmission network is expanded in stages. The dynamic characteristic should be considered. Aiming at these issues, a multistage fuzzy-robust TNEP model taking into account interval model of renewable power and fuzzy model of predicted load is proposed based on the expanded fuzzy chance constrained programming. In accordance with the feature of the established planning model, the model is transferred to a multistage robust TNEP model considering interval uncertainty model of renewable power and predicted load. Thus, the calculation burden for solving the planning model decreases. A hybrid solving strategy is developed to solve the model, in which the particle swarm optimization (PSO) algorithm is to determine number of new lines in each corridor and the improved branch-and-bound (IBB) algorithm is used to check the security of the planning schemes. The analyses on the modified IEEE RTS 24-bus system and a 231-bus system verify the effectiveness and adaptability of the proposed method.
© 2019 John Wiley & Sons, Ltd.

The explosive growth of wind power is accompanied with the problem of disorderly construction, which is difficult to take into consideration in planning stage and would lead to high cost of power grid expansion. To cope with the uncertainty of wind power construction, this paper proposed a coordinated flexible planning method based on the hi-level optimization model. A probabilistic method is used to analyze uncertainty of wind power. Considering variable flexible resources from generation, grid, demand side and energy storage, a hi-level optimization model is built for planning, with the upper layer making investment decisions considering both wind accommodation benefit and economic cost and lower layer simulating operation status in detail and maximizing flexibility supply. A maximum net benefit/increment ratio method is used to solve the model, basing on the unified evaluation indicator of different flexible resources. Case study shows that the bi-level model and the planning method can take wind power construction uncertainty into consideration and obtain an economic and credible planning result, thus achieving efficient accommodation of renewable energy.

The benefits of transmission-distribution interactions at boundary buses are gradually unlocked in a restructured power network. Since the roles of transmission system planner and distribution system planner have some similarities in maintaining the economy and security of their systems, the transmission and distribution systems should collaborate and coordinate with each other to determine the expansion scheme in a positive and active manner. To address this problem, this paper presents a decomposition based expansion method for hybrid transmission and distribution networks (HTDNs), which simultaneously optimizes the hourly generation schedule. The original centralized optimization problem is decomposed into a transmission expansion subproblem encompassing numbers of distribution expansion subproblems. A distributed solution technique is proposed to guarantee shared variable consistencies. Case studies carried out on a HTDN, denoted as T24D9, validate the effectiveness and high performance of the formulated expansion problem. © 2019 IEEE.