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Abstract In this paper, optimal strategies are proposed for electric vehicles in charging stations to participate in the secondary frequency regulation, while considering their charging demands. In order to fairly allocate the dispatch from the control center among electric vehicles according to their charging demands, two optimal real-time strategies are proposed, respectively based on area control error and area regulation requirement. With the proposed strategies, the expected charging of electric vehicles is optimally tracked in real time by using the regulation task from the control center. Simulations on a two-area interconnected power grid show that the proposed two strategies can respectively lead to a 12.66% and 16.78% frequency deviation reduction and a 13.76% and 9.86% generator regulation reduction. At the same time, the charging demands of EVs can also be ensured.

Abstract Load restoration is an important issue for power system restoration after a blackout. A second order conic programming (SOCP) model is proposed based on the information gap decision theory (IGDT) to maximize load pickup considering the uncertainty of load increment. Because distribution functions of load increment are difficult to obtain, the optimization of load pickup is transformed to maximize the fluctuation range of load increment by the IGDT. The derived optimal fluctuation range can ensure that the reenergized system is secure, and the amount of load pickup is always better than the specified expectation. Moreover, because the optimization model of the fluctuation range is a mixed-integer nonlinear model which is challenging to solve accurately and efficiently, the nonlinear model is transformed into a SOCP model that can be efficiently solved using CPLEX. The efficiency of the IGDT-based SOCP model is validated using the New England (10-machine 39-bus) system. The simulation results show that the derived load pickup shows expected robustness with respect to the load increment uncertainty.

Abstract For wind energy conversion systems operating above the rated wind speed, the frequent pitch actions regulate the mechanical power as the rated one with the cost of blade and drive shaft loads. It is meaningful to maintain the desired power with appropriate pitch sensitivity related to the wind speed fluctuations, which can further reduce the mechanical loads of wind turbines with a longer service life. To quantify the blade pitch sensitivity, the blade pitch standard deviation is introduced to connect the pitch actions with the blade and drive shaft loads. Within a variable-weight model predictive control (MPC) strategy, both generator power output quality and load conditions are optimized through the pitch/torque participation coordination based on the Pareto analysis. Moreover, the MPC-weight matrix could be updated adaptively through the wind status assessment. The comparisons between the proposed strategy and the traditional gain scheduling PI one show the effectiveness. Several suggestions are also concluded for industrial wind turbines with MPC implementations.

Abstract This paper proposes a distributed voltage control (DVC) scheme for smart distribution networks with high penetration of inverter-based distributed generators (DGs), aiming to optimally coordinate DG units and on load tap changer (OLTC) transformer to regulate the voltages within the feasible range. The proposed scheme consists of two important parts: (1) distributed information synchronization (DIS) framework and (2) distributed model predictive control (DMPC)-based voltage control scheme. The DIS framework is established based on the consensus protocols to synchronize the specific information about the critical bus voltages and potential OLTC actions. The DMPC-based voltage control scheme is presented, in which each DG unit only exchanges information with its immediate neighbors and solves the local optimal control problem. Two control modes are designed to better deal with different operating conditions. In the normal mode, only the reactive power outputs of DG units are optimized to mitigate the voltage deviations. In the corrective mode, both of the active and reactive power outputs of DG units are optimally controlled to correct the severe voltage deviations. To mitigate the mutual interaction between the DGs and OLTC, the potential actions of OLTC are predicted and considered in the optimization problem of each units. The control performance of the proposed scheme was demonstrated using a real medium-voltage (MV) distribution network with two feeders under both normal and large-disturbance conditions.

This paper focuses on economic dispatch (ED) in power systems with intermittent wind power, which is a very critical issue in future power systems. A stochastic ED problem is formed based on the recently proposed versatile probability distribution of wind power. The problem is then analyzed and proved to be strictly convex. Although such convex optimization is tractable in many cases, it may take a long time to solve due to its large scale. This paper proposes a dual decomposition method to decompose the large problem. Then, two methods are employed to solve the decomposed problem, namely, the subgradient method and a faster method, limited-memory BFGS with box constraints (L-BFGS-B, a quasi-Newton method). Case studies were conducted to verify the efficiency of the dual decomposition and L-BFGS-B method for solving the stochastic ED problem.

Abstract The power system operators are facing significant challenges in the operation due to the increasing penetration level of renewable energy sources (RESs). The flexibility from the district heating system (DHS) is attracting considerable interest to deal with RES uncertainty. This paper formulates a distributionally robust chance-constrained (DRCC) optimization model of the integrated electricity and heating system (IEHS) dispatch to hedge the uncertainty of RES and exploit the flexibility of the DHS. In particular, the uncertainty from the electrical power system (EPS) is propagated to the DHS so that both systems can respond to the uncertainty of RES. The uncertainty of the wind power is modeled by an ambiguity set, which defines a family of probability distributions with the same first and second-moment property. Real-time regulation actions of both the EPS and DHS to respond to the wind power forecast errors are modeled through the data-driven affine control policies. To achieve computational tractability, the proposed DRCC model is reformulated as a second-order cone program (SOCP). The simulation results tested on the integrated six-bus and seven-node system demonstrate that the proposed DRCC model outperforms the chance-constraints dispatch based on Gaussian distribution for the secure operation of the IEHS.