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Renewable energy sources are a significant means of addressing dwindling conventional fuels and have been increasingly incorporated in transmission power grids on a large scale. However, the uncertainty of renewable energy source power outputs threatens the voltage security of power grids, which restricts the full integration of these sources to some degree. To address this problem, this work employs the reactive power optimization incorporating interval uncertainties (RPOIU) model for the transmission systems. In the RPOIU model, state variables, including load voltages, reactive power generation, and bus angles, are regarded as variables with interval values. Meanwhile, to account for the fluctuation of the load demand and the renewable energy sources in multiple periods of one day, a multi-time RPOIU is proposed. The model seeks to obtain a day-ahead optimal voltage control strategy for ensuring that interval state variables remain within their operating limit constraints in each period of one day, as well for maintaining a low level of daily real power losses. To solve the multi-time RPOIU model, we propose the interval sequential quadratic programming method (ISQPM), which employs a second-order interval Taylor expansion. In addition, an improved interval power flow calculation method, i.e., the optimizing-scenarios method, is employed to obtain the accurate ranges of the interval variables, thus improving the convergence performance of the ISQPM. The quadratic penalty function incorporating the rounding-off process is proposed to deal with the discrete variables in the multi-time RPOIU model. Three cases are tested by simulation to demonstrate the effectiveness, good applicability, and robustness of the proposed method, particularly in comparison to the previously proposed linear approximation method. [ABSTRACT FROM AUTHOR]

Renewable energy sources provide an important means of reducing reliance on conventional fuels. However, some renewable energy resources such as wind and solar energies are intermittent, and their uncertainty threatens the operating security of the power grid. To solve this problem, this study proposes the use of intervals to model the power output of renewable energy resources and the power load demand, and accordingly develops an interval voltage control model, i.e. interval reactive power optimisation model. The proposed model considers the control modes of renewable energy power generators and can safeguard the security of power grids by ensuring that the voltages reside within established limits. An adaptive genetic algorithm is employed to solve the proposed model, where a newly developed interval power‐flow (IPF) calculation is used to solve the IPF equations, and penalty functions are applied to express inequality constraints. The proposed method is introduced in detail, and simulation results are presented to demonstrate its performance in comparison with a previously proposed interval voltage control method, as well as its applicability to large systems with various fluctuations of input data. The proposed approach provides robust convergence, obtains lower system power losses, and substantially reduces the computation time. [ABSTRACT FROM AUTHOR]

Recent trends in the increasing penetration of renewable energy generation and the increasingly massive integration of electric vehicles (EVs) into multiple-microgrid (MMG) systems pose tremendous challenges to the effective energy management of these systems. However, the solutions of current energy management methods have the disadvantage of excessively limited robustness against uncertain fluctuations in renewable energy generation, and existing hierarchical EV scheduling methods are solved via an iterative process that is prohibitively time-consuming for real-time practical applications. This work addresses the abovementioned issues by constructing an improved data-driven polyhedral uncertainty set based on modified self-organizing feature map neural networks that perform data clustering. In addition, an iteration-free hierarchical framework is proposed for conducting the tri-layer energy management of MMGs with the massive integration of EVs. Specifically, the global positive power factor of EV aggregation is proposed to formulate a set of constraints into the upper-layer model of the tri-layer framework, which can guarantee the feasibility of the middle-and-lower layer models. The value of this factor is determined using a multi-objective optimization algorithm that makes a trade-off between the feasible range of the output of EV aggregators and the degree to which the trip demands of EVs are violated. The superiority and effectiveness of the proposed iteration-free robust hierarchical energy management method for MMGs are verified via simulations involving a practical MMG under high penetrations of renewable energy generation and massive integration of EVs. • Propose the iteration-free hierarchical scheduling method for addressing tri-layer energy management problems for MMGs with massive EVs. • The global positive power factor (GPPF) defined in the upper layer model can ensure the solution of the lower layer model is always feasible without iteration. • The GPPF value enables MG operators to flexibly select a tradeoff for the GPPF based on their operational experiences. • The novel time-series-enabled data-driven polyhedral uncertainty set can produce solutions with better robustness against uncertainties. [ABSTRACT FROM AUTHOR]

High penetrations of renewable energy sources incorporated into transmission systems pose a substantial challenge to transmission expansion planning operations due to both the strong uncertainty of these sources and their high variability. However, most current robust transmission expansion planning studies focus exclusively on the issue of uncertainty, and neglect to optimize system flexibility in an effort to cope with the high variability of these sources. The present work addresses this critical issue by proposing a flexibility-oriented robust transmission expansion planning method developed according to the unit commitment characteristics of coal-fired and gas-fired generation units that fully considers the short-term flexibility requirements of electric power systems while maintaining high robustness to the uncertainties of RES outputs in long-term transmission expansion planning problems. The proposed complex model with unit commitment constraints containing massive binary variables is solved by first reducing the number of binary variables via the application of clustering techniques to identify generator units with similar generation properties, and then decreasing the complexity of the model further by relaxing the integer variables in the unit-clustered model. Finally, we develop generalized column-and-constraint generation algorithms that can solve the clustered model and the relaxed simplified model with greatly enhanced efficiency. Comparisons of the numerical results obtained by the proposed approach with existing state-of-the-art methods when applied to a simple Garver 6-node system and a realistically-sized power system demonstrate that the proposed method produces optimal RTEP solutions that account for both the uncertainty and variability of RES outputs. Moreover, the total cost of the proposed approach is 2.22 × 1 0 6 $ less than that of the other state-of-the-art methods considered, which is of great significance in guiding practical transmission expansion planning applications. • Neglecting flexibility of units in robust transmission planning models can result in significant errors. • Existing algorithms fail to solve the flexibility-oriented planning model for large-scale systems. • Transmission planning schemes are significantly influenced by flexibility-related constraints. • The proposed generalized algorithm expedites the solution process of the model. • The relaxation technique facilitates the applicability of the flexibility-oriented model to large-scale systems. [ABSTRACT FROM AUTHOR]

The rapid incorporation of wind power resources in electrical power networks has significantly increased the volatility of transmission systems due to the inherent uncertainty associated with wind power. This paper addresses this issue by proposing a transmission network expansion planning (TEP) model that integrates wind power resources, and that seeks to minimize the sum of investment costs and operation costs while accounting for the costs associated with the pollution emissions of generator infrastructure. Auxiliary relaxation variables are introduced to transform the established model into a mixed integer linear programming problem. Furthermore, the novel concept of extreme wind power scenarios is defined, theoretically justified, and then employed to establish a two-stage robust TEP method. The decision-making variables of prospective transmission lines are determined in the first stage, so as to ensure that the operating variables in the second stage can adapt to wind power fluctuations. A Benders’ decomposition algorithm is developed to solve the proposed two-stage model. Finally, extensive numerical studies are conducted with Garver’s 6-bus system, a modified IEEE RTS79 system and IEEE 118-bus system, and the computational results demonstrate the effectiveness and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

Characterizing the integration flexibility of renewable distributed energy resources (RDERs), which describes the feasible region of RDER accommodations with which the operational constraints of an active distribution network (ADN) can be respected, facilitates the ADN operations. However, traditional characterization methods face the challenge of concurrently dealing with inevitable AC power flow and numerous RDERs. To resolve this issue, this paper develops a novel characterization method that contains two interconnected modules: learning and updating modules. The two modules jointly and iteratively calculate the parameters to characterize the integration flexibility of RDERs (IFR). In the learning module, a new loss function is designed to train the IFR parameters using a training data set. In the updating module, two optimization problems are developed based on the optimization-based performance estimation of the trained parameters to generate more data points for performance improvement in the learning module. The effectiveness of the proposed method is corroborated in the IEEE 33-bus and IEEE 136-bus test systems. • A novel characterization framework for the IFR with learning and updating modules. • A new loss function in the learning module from the physical information of the IFR. • A strategy in the updating module for the improved characterization of the IFR. • A better tradeoff between security and conservatism of the IFR characterization. • Significant potential in violation reduction under incorrect justifications. [ABSTRACT FROM AUTHOR]